Variable interactivity is crucial in biological multivariate time series analysis. This research suggests using graph structures to represent such interactions for more explainable decision-making processes. However, measuring the variable interaction in a graph is an open problem with no unique solution. Existing graph construction methods are either computationally costly, require extensive training, or disregard the inherent data nonlinearities and nonstationarity. We propose using the Fuzzy Synchronization Likelihood (FSL) criterion to address these challenges in constructing a graph and examining the qualitative similarity and dependency among variables. We propose applying this strategy to automated rehabilitation exercise evaluations based on human joint motion data. This multivariate time series application benefits from FSL-constructed graphs by offering further insight into the kinematics of joint interactions. Finally, we extend the convolutional layer in the Deep Mixture Density Neural Network (DMDN) to process the FSL-constructed graph, extracting practical information regarding task-based variable dependencies. An ablation study shows that the proposed FSL Graph-based Deep Neural Network (FSLGDN) outperforms its competitive approaches that use linear correlations and human anatomy for graph construction. Results also indicate that task-based consideration of joint motion data interactions is more beneficial than anatomy. Furthermore, the inherent nonstationarity of motion data leads to the extraction of more information than its linear correlation counterpart. Finally, while the proposed approach ranks competitively with a DMDN, the proposed approach's graph construction and representation of feature dependencies are more intuitive, leading to more explainable decision-making processes.

Motor imagery (MI) analysis from EEG signals constitutes a class of emerging brain-computer interface (BCI) applications that face EEG's predominant complexities arising from the multitude of channels and the vast number of possible features. This study presents a two-step multiobjective set-based integer-coded fuzzy-initialized evolutionary algorithm (MOSIFE) for efficient EEG-based MI signal analysis. The two-step process is a non-dominant wrapper strategy that sequentially identifies the optimal channels and the minimal set of features, thereby reducing MI's combinatorial search complexity. We also employ a reptile-based search algorithm (RSA), a recent metaheuristic for efficient search in multimodal continuous domains, to optimize the classifier's hyper-parameters. The proposed MOSIFE-RSA algorithm is benchmarked against 12 representative algorithms on four standard BCI Competition databases, including IV-I, III-IVa, III-IIIa, and II. The results show that MOSIFE-RSA improves accuracy by 20%, with channel selection contributing as much as 15% and feature selection as much as 5% towards these results. Furthermore, it reduces computational complexity by 81% through channel selection and 16% through feature selection, demonstrating its effectiveness in advancing EEG-based MI signal analysis. This research has practical implications for developing more accurate and efficient brain-computer interface systems.

Filters and wrappers represent two mainstream approaches to feature selection (FS). Although evolutionary wrapper-based FS outperforms filters in addressing real-world classification problems, extending these methods to high-dimensional, many-objective optimization problems with imbalanced data poses substantial challenges. Overcoming computational costs and identifying suitable performance metrics are vital for navigating search operation complexities. Here, we propose using the Jaccard similarity (JS) in a set-based evolutionary many-objective (JSEMO) FS search, addressing both evolutionary FS and imbalanced classifier choice concurrently. This study highlights the mutual influence between these aspects, impacting overall algorithm performance. JSEMO integrates JS into population initialization, reproduction, and elitism steps, enhancing diversity and avoiding duplicate solutions. The set-based variation operator utilizes intersection and union operators for compatibility with binary coding. We also introduce a double-weighted KNN (KNN2W) classifier with four supportive objectives as a many-objective FS problem to handle imbalanced distributions. Compared with 20 methods on 15 benchmark problems, JSEMO produces distinct optimal features, significantly improving overall accuracy, balance accuracy, and g-mean metrics with comparable feature set size and computational cost. The ablation study underscores the positive impact of all JSEMO components, highlighting the set-based variation operation with JS and KNN2W with relevant evaluation metrics as the most influential aspects.

Fuzzy neural networks (FNNs) are synergistic structures that aim to benefit from the properties of fuzzy logic in neural network structures. Yet, the traditional FNNs do not explicitly address the reliability aspect of uncertain real-world applications. Here we propose a reliable fuzzy neural networks (ReFNNs) in which an information reliability measure is employed for rule training and robust decision making of the uncertain input data. The universal approximation property of the proposed structure is proved using the Stone-Weierstrass theorem. Furthermore, the resulting structure is continuous and differentiable. Hence, a backpropagation training algorithm is developed to optimize the proposed ReFNN's parameters. Additionally, asymptotic stability analysis based on the Lyapunov theorem is shown for ReFNNs. Finally, this structure is first evaluated with several basic benchmark examples in function approximation (sine, increasing sinusoid, quadratic Hermite, and nonlinear functions). We then apply it to modeling several benchmark nonlinear systems (including a 3rd order nonlinear dynamical system, a continuous stirred tank reactor, a two-cascaded tank problem, a Wiener-Hammerstein system, and wind speed prediction) and the adaptive control of nonlinear systems in both direct and indirect frameworks. Results confirm the superiority of the proposed structure over traditional FNNs in terms of error and sensitivity in the presence of noise.

Oxygen deficiency is a serious health problem that may occur as a result of many diseases. In this article, we present an influence-based nano fuzzy swarm (INFS) for oxygen deficiency detection and therapy using a swarm of oxygen carrier nanomachines operating in three cognitive fields of control, influence, and interest. In particular, we propose a long short-term memory (LSTM) deep neural network for detecting apnea by analyzing the irregular peripheral oxygen saturation (SpO2) signal. Using the proposed sleep-in-the-loop strategy and the desaturated blood biomarkers, including oxygen and hydrogen ion concentrations, an in-silico multithreshold nano fuzzy swarm noninvasive therapeutic method is then performed. We also analytically prove the stability of the INFS using swarm control theory. We apply our strategy to sleep apnea, as one of the most common instances of oxygen deficiency. Furthermore, we compare the accuracy of INSF by using LSTM, bidirectional LSTM (BiLSTM), multilayer perceptron (MLP), convolutional neural network (CNN), and support vector machines (SVM). The detection and therapy results are then compared with other apnea detection methods. The input variables and structure of INSF, i.e., the number of rules and width of membership functions, are studied in terms of robustness to noise. As the results show, the proposed artificial intelligence (AI)-based noninvasive nano detection and therapy method could outperform the competing approaches in treating oxygen deficiency emergencies such as apnea.

Fuzzy associative classifiers (FACs) have recently received considerable attention in the data mining community due to their ability to address the imprecision and graduality of truth. Similar to their more traditional statistical peers, these classifiers, however, have remained largely data driven, not leveraging human knowledge to their advantage. This is while human expert opinion and intuition should be a unique vantage point for such systems. We introduce here, for the first time, a human-centered framework (FLeAC) for FACs based on extended fuzzy logic and f -transformation that uses experts' opinions and preferences along with statistical data to solve subjective real-world problems. In FLeAC, experts take part in both constructing and reasoning of the classifier by assigning linguistic validity to each item. These validities are then aggregated using collective intelligence that determines final item validity. To examine the proposed framework, we extend an efficient and well-known FAC, CFAR, and present an extended f -CFAR algorithm. Also, several variations of f -CFAR are implemented to examine the effect of rule validity and different f -transformation operators. We then run various nonparametric statistical tests, including Friedman, Nemenyi posthoc, and ROC tests on an actual medical dataset of burn patients from Ahwaz, Iran, to compare f -CFAR performance with those of the original and nine other rule-based classifiers. Statistical analysis shows that f -CFAR not only has a better overall diagnostic performance than CFAR but also it outperforms CFAR and the other rule-based classifiers in terms of the number of rules, the number of conditions, and the execution time, leading to a more compact and comprehensible classifier with comparable accuracy.

Human-centered systems of systems, such as social networks, the Internet of Things, or healthcare systems are growingly becoming significant facets of modern life. Realistic models of human behavior in such systems play an essential role in their accurate modeling and prediction. Nevertheless, human behavior under uncertainty often violates the predictions by the conventional probabilistic models. Recently, quantum-like decision theories have shown a considerable potential to explain the contradictions in human behavior by applying quantum probabilities. But providing a quantum-like decision theory that could predict rather than describe the current state of human behavior is still one of the unsolved challenges. The fundamental contribution of this work is introducing the concept of entanglement from quantum information theory to Bayesian networks (BNs). This concept leads to an entangled quantum-like BN (QBN), in which each human is a part of the entire society. Accordingly, society's effect on the dynamic evolution of the decision-making process, which is less often considered in decision theories, is modeled by entanglement measures. To reach this aim, we introduce a quantum-like witness and find the relationship between this witness and the famous concurrence entanglement measure. The proposed predictive entangled QBN (PEQBN) is evaluated on 22 experimental tasks. Results confirm that PEQBN provides more realistic predictions of human decisions under uncertainty when compared with classical BNs and three recent quantum-like approaches.

Sensing and perception is generally a challenging aspect of decision-making. In the nanoscale, however, these processes face further complications due to the physical limitations of devising the nanomachines with more limited perception, more noise, and fewer sensors. There is, hence, higher dependence on swarm sensing and perception of many nanomachines. Here, taking hardware and software bioinspiration, we propose Chemo-Mechanical Cancer-Inspired Swarm Perception (CMCISP) based on online nano fuzzy haptic feedback for early disease diagnosis and targeted therapy. Particularly, we use epithelial cancer cell’s scaffold as a carrier, its properties as a distributed perception mechanism, and its motility patterns as the swarm movements such as anti-durotaxis, blebbing, and chemotaxis. We implement the in-silico model of CMCISP using a hybrid computational framework of the cellular Potts model, swarm intelligence, and fuzzy decision-making. Furthermore, the target convergence of CMCISP is analytically proved using swarm control theory. Finally, several numerical experiments and validations for cancer target therapy, cellular stiffness measurement, anti-durotaxis movement, and robustness analysis are also conducted and compared with a mathematical chemotherapy model and authors’ previous works on targeted therapy. Results show improvements of up to 57.49% in early cancer detection, 26.64% in target convergence, and 68.01% in increased normoxic cell density. The study also reveals the strategy’s robustness to environmental/sensory noise by applying six SNR levels of 0, 2, 5, 10, 30, and 50 dB, with an average diagnosis error of only 0.98% and at most 2.51%.

Social things face great uncertainty and complexity in their decision-making. This is true whether the social thing is as large as the electric grid of a country or as small as drug carrier targeted nanomachines employed for cancer treatment. The problem is further complicated when it is tasked with serving humans, with the intricate and ill-defined meaning of service. Therefore, there is a need for cognitive decision-making in which human factors of time, attitude, attention, trust, and bias are considered in the recommendation, prediction, analysis, estimation, and automated decision making. Here, we introduce a trust-based decision-making architecture for swarms of bioinspired nanomachines. Particularly, the factor of trust is used as an index for swarm joining and disjoining. Each nanomachine's decision is considered as a new attitude that is weakened or reinforced by a trust factor. The trust factor is derived using a Fuzzy Cognitive Map which is composed of integrity, competence, consistency, loyalty, and openness. Nanomachines with high trust factors form a dense group and change to a “trustee” swarm. The trustee converges to the cancer site. The result shows the proposed method in targeted drug delivery outperforms the competing strategies with lower hypoxic and endothelial cell density as the marker of cancer.

Z-numbers consist of two components, restriction and restriction reliability, to cover both possibilistic and probabilistic uncertainties. So far, the components of Z-numbers are merely determined by expert knowledge and lack automated learning/training. To overcome this limitation, we propose a Z-Adaptive Fuzzy Inference System (ZAFIS) that systematically learns the parameters of Z-numbers from input-output data pairs. We first convert the second component of Z-numbers to a crisp number. We then use this number as a weight for the first fuzzy membership part of Z-numbers. Finally, the resultant membership is placed in a fuzzy inference system, and the parameters of the system are learned based on the input-output data pairs using a gradient descent algorithm. The proposed method is evaluated on several functions (sine, increasing sine, Hermite, Gabor, and a nonlinear function) with/without added noise scenarios. The results show that the ZAFIS is more robust against the noisy inputs and is superior to the Fuzzy Inference Systems (FISs) in terms of MSE.

A realistic control strategy for stochastic multi-agent systems (MAS) should consider uncertainties such as unknown dynamics of the agents, limited accessibility of the leader state, and external disturbances. Here, we propose an adaptive fuzzy control structure for a class of stochastic uncertain MAS that have partially unknown heterogeneous higher-order dynamics of the agents. Artificial potential functions (APFs) are incorporated to model the communication among the agents, and the unknown dynamics are approximated by the aid of fuzzy logic systems. The current work extends our previous work to the case where only some of the agents could access the states of the leader to reach an n-dimensional consensus on the leader states. The overall system’s stability is assured using Lyapunov stability theory, and all of the closed-loop signals of the system are shown to be semi-globally uniformly ultimately bounded (SGUUB) in the mean square. Simulation results ensure the capability of the system in low tracking error with reasonable control input usage.

Goal: Human voice is a promising noninvasive indicator for diagnosing Parkinson's Disease (PD). It is also unique since it can be collected remotely, increasing accessibility to a wide range of underprivileged patients. However, recognizing PD's signature in the human voice is nontrivial since the available features are many, and the signal may be noisy. Methods: A new mechanism based on Interval Type-2 Fuzzy Analytical Hierarchy Process is proposed here for choosing a reduced feature set from 339 dysphonia speech features, based on five criteria of 1) Robustness, 2) Relief, 3) Minimum Redundancy and Maximum Relevance, 4) Gaussian mixture model separation, and 5) Classifier separation ability. A Least Squares Support Vector Machine then categorizes the samples as belonging to either a healthy subject or a patient with PD. The database of 47 subjects with an average age of 67 is obtained from the elderly in nursing homes and Parkinson's specialized clinics. By reducing signal quality similar to a standard phone line, we study the teleoperation prospect of the proposed technique. Results: Ten-fold cross-validation shows an overall accuracy of 95.32%(93.11%) for noiseless(noisy) conditions, with separate analysis for male, female, and both genders populations. Furthermore, Leave-One-Speaker-Out analysis yields an overall accuracy of 93.11%(84.61%) for noiseless(noisy) conditions. Conclusion: The proposed strategy offers viable remote PD diagnosis with higher accuracy for the male population. Significance: The proposed method suggests reduced feature sets that meet differing objectives of simplicity, performance, and robustness. Results could be particularly significant in PD diagnosis in remote areas.

The existing approaches to interpretability in data-driven fuzzy models are reliant, heavily, on constraining different components of the model, and subsequently on sophisticated learning methods. This article, however, puts forward an alternative view of the interpretability of fuzzy models based on the concept of specificity. In addition to being free of constraints, this view can effectively deal with the subjectivity inherent in the linguistic interpretation of fuzzy sets and rules rather than suppressing it, as the constraint-based approaches do. Second to this semantic improvement, a specificity-based hierarchical fuzzy structure is also proposed to deal with the complexity-based aspect of interpretability. It achieves this purpose by means of a two-level hierarchy, in which a number of general rules at the first level localize both the inference and learning processes of the second level to a small number of relevant, specific rules. Such a localization, besides employment of the recursive least squares algorithm for parameter learning, reduces the computational burden of the model significantly, making it capable of real-time operation. The experimental results of applying the proposed fuzzy modeling approach to a variety of synthetic and real-world datasets confirm its efficacy in the sense of interpretability, accuracy, and computational simplicity.

Cooperative control of multi-agent systems deals with various challenges such as uncertain dynamics, external disturbances, and limitations on interactions with other agents or the leader. Here, a cooperative adaptive fuzzy controller is introduced for a class of higher-order affine nonlinear multi-agent systems. The agents can only interact with their neighbors, and only a portion of them have access to the leader states. The unknown dynamics of the agents are approximated by fuzzy systems. In addition, the artificial potential functions (APFs) are employed to model the interaction among the agents. The APFs could provide a more transparent and straightforward design in comparison with usual graph-based methods. By modifying the error function, the proposed controller extends our previous work by giving only a portion of the agents access to the leader states. Lyapunov stability theory is used to design appropriate update laws for the weights of the fuzzy system and verify the overall stability of the system. Simulations results indicate a lower steady- state tracking error and lesser control energy consumption compared with a competing neural network-based approach.

Controlling stochastic multi-agent systems (MAS) is a promising branch of systems engineering owing to its ability to solve complex practical problems by taking inspiration from the social behavior of living organisms. Consensus on more than one state is a less considered problem in the framework of leader-following control of high-order uncertain nonlinear stochastic multi-agent systems with unknown dynamics. To this end, in this paper, a sliding surface integrates a tracking error polynomial with consensus error between states of the connected agents. Accordingly, a new distributed fuzzy sliding mode controller is designed for a class of stochastic multi-agent systems with partially unknown deterministic dynamics and unknown bounded stochastic dynamics. The proposed control protocol steers agents to reach agreement on states of the leader and concurrently approximates the unknown part of their deterministic dynamics by using fuzzy systems with adaptive parameters. In the sense of Lyapunov, it is proved that consensus error and all of the closed-loop signals of each agent are semi-globally uniformly ultimately bounded (SGUUB) in mean square. Under the designed controller, nonlinear closedloop experimental dynamics behave well in both tracking and reaching state consensus.

This paper proposes a novel optimization algorithm for read-coded problems called the Probabilistic Optimization Algorithm (POA). In the proposed algorithm, rather than a binary or integer, a probabilistic representation is used for the individuals. Each individual in the proposed algorithm is a probability density function and is capable of representing the entire search space simultaneously. In the search process, each solution performs as a local search and climbs the local optima, and at the same time, the interaction among the probabilistic individuals in the population offers a global search. The parameters of the proposed algorithm are studied in this paper and their effect on the search process is presented. A structured population is proposed for the algorithm and the effect of different structures is analyzed. The algorithm is used to solve Latin Hyper-cube problem and experimental studies suggest promising results. Different benchmark functions are also used to test the algorithm and results are presented. The analyses suggest that the improvement is more significant for large scale problems.

Opinion formation in social networks is an interesting dynamical process from the perspective of system modeling due to its large scale as well as the variety of structural and parametric uncertainties that it entails. This paper proposes a probabilistic fuzzy opinion formation model for predicting the opinions of communities in the social networks. In this regard, the opinions of a group of individuals about a given topic in a Telegram pilot group, as a popular social network, are collected and presented in the framework of the probabilistic fuzzy model. Based on the obtained data, the parameters of the model are extracted, and the model is tuned. Finally, the variations of the actual opinions throughout time are compared with the model predictions. The numerical results in this study show that, with appropriately tuned parameters, the model successfully represents the opinion formation process, with an average error that approaches zero.

Cancer is a complex disease which is composed of numerous interactive components that operate in an environment with high uncertainties. Approaching such a system hence requires its own set of tools. Here, we propose an in-silico model of cancer cell line based on local environmental information, and study its emergent complex global cellular behaviors. The proposed model is composed of a Fuzzy Inference System (FIS) and a Cellular Automata (CA). The FIS has five inputs including nutrition concentration, cell density, apoptosis rate, Vascular Endothelial Growth Factor (VEGF) concentration, and H+ion concentration. The output of the proposed FIS is the cell state parameter. The FIS determines a threshold as the input of the CA for extracting the appropriate cellular patterns. The generated cellular patterns are based only on local information, but the overall system exhibits a global behavior. The merit of the model is in the simplicity of the rules in learning the complex global system behaviors.

Nowadays, humans can play an important role in control of robots. Some researches have used signals that coming directly from humans for control interfaces. In this paper, electromyogram (EMG) signals from the muscles of the human's upper limb are used as the control interface between the user and a robot arm. A Multi-Layer Perceptron (MLP) is trained by additional unsupervised pre-training to decode upper limb motion from kinematic data and EMG recordings. On the other hand, the control structure differs from previous ones because using the voltage control strategy instead of the torque control strategy. The common control structure for elastic-joint robots employs two control loops whereas this controller has only one control loop and actuators are considered in the dynamic equation of the robot. The proposed control design is verified by stability analysis and experimental results demonstrate the effectiveness of this controller.

The concepts of sets and approximate reasoning within extended fuzzy logic (FLe) provide a systematic procedure for transforming unprecisiated knowledge into a nonlinear mapping over what we define here as f-sets. An f-set differs from a fuzzy set in that it is associated with the restriction of validity in addition to that of possibility. Therefore, by f-set, we can simultaneously deal with two different types of uncertainties: one that is related to ill-known objects represented by incomplete information-information with its one or more aspects being imprecise/vague/partial/nonspecific/undetermined-and another that is related to truth values considering gradualness. Here, we define new concepts of ϑ-cuts and αϑ-cuts, introduce the fextension principle, and consider arithmetic computations within FLe. We then address other aspects of the proposed FLe system such as fuzzification and validification operations in input processing stage, set-conversion and defuzzification in output processing stage, and inferencing. In fact, in this paper, we intend to develop FLe theoretically and practically from the stands of sets and systems to extend the concept of approximate reasoning. As a consequence of this development, we assert that considering the validity degree of methods and information can lead to more reasonable and trustworthy results through capturing more uncertainty.

In multi agent systems, agents are supposed to be aware of each other and need to cooperate with their neighboring agents in order to reach their objectives. However, there may be some selfish agents that reject cooperation due to the cost of performing the requested task, even while taking benefit from network resources and using intermediate agents to send their requests. The existence of these nodes cannot be ignored; and system efficiency can significantly deteriorate as the number of such nodes increases. Thus, a special mechanism is needed in order to confront them. Here, local structural update based on fuzzy social plasticity is proposed to persuade cooperative behavior. In this mechanism, relationships with non-cooperative agents are disconnected and the more cooperative ones, i.e. those with a trusting history for cooperation, and closer agents are preferred for the chain of relationship by a fuzzy logic heuristic. The proposed strategy intensifies cooperation by diminishing the impact of non-cooperation and reinforcing that of the cooperation. The strategy is evaluated on some random networks with different cooperation rates. Results confirm increased network cooperation, even when the majority of the agents are non-cooperative.

This paper proposes a reliable and efficient method for recognition in two different orientations (either left or right) by Magnetoencephalograph (MEG) signals. The brain activities are measured using different approaches with different spatial and temporal resolutions. The MEG signals are usually used for brain-computer interface (BCI) applications due to high temporal resolution. The MEG signals were recorded from different brain regions of four different human subjects during visual covert selective spatial attention task. The hybrid method proposes pre-processing; feature extraction by Hurst exponent, Morlet wavelet coefficients, and Petrosian fractal dimension; normalization; feature selection by p-value; and classification by support vector machine (SVM) and fuzzy support vector machine (FSVM). The results show that the proposed method can predict the location of the attended stimulus with a high accuracy of 91.62% and 92.28% for two different orientations with SVM and FSVM, respectively. Finally, these methods can be useful for BCI applications based on visual covert selective spatial attention.

Nowadays, we are facing the rapidly growing amount of data being produced in many organizations, social networks and internet. These data are generated in disparate locations and their aggregation into one location is exceedingly time and space consuming. Traditional statistical methods are not sufficient for processing of this massive multi-source data. In this paper, we propose a new fuzzy-based decision fusion approach for classification problems of this kind. The necessity of fuzzy information arises in distributed classification because imprecision, uncertainty and ambiguity can be found at all information sources, from the data itself to the results of the classifiers. In the proposed approach, multiple classifiers are constructed based on different information sources which have different degrees of reliability. Then a fuzzy rule based system is designed for approximating distribution of reliabilities of sources over the input space. The decision fusion of multiple classifiers takes place using the estimated degrees of sources' reliabilities. Comparison results are made between both centralized classification and two other distributed classification methods. One is averaging and the other is discounting each classifier's decision based on its accuracy. Results show the high accuracy of the proposed method in making decisions in distributed environments, without the overhead of aggregating the entire data in one location.

Recently, clustering has been used for preprocessing datasets before applying classification algorithms in order to enhance classification efficiency. A strong clustered dataset as input to classification algorithms can significantly improve computation time. This can be particularly useful in Big Data where computation time is equally or more important than accuracy. However, there is a trade-off between speed and accuracy among clustering algorithms. Specifically, general type-2 fuzzy c-means (GT2 FCM) is considered to be a highly accurate clustering approach, but it is computationally intensive. To improve its computation time we propose here a hybrid clustering algorithm called KGT2FCM that combines GT2 FCM with a fast k-means algorithm for input data preprocessing of classification algorithms. The proposed algorithm shows improved computation time when compared with GT2 FCM as well as KFGT2FCM on five benchmarks from UCI library.

Emotional neural network (ENN) is a recently developed methodology that uses simulated emotions aiding its learning process. ENN is motivated by neurophysiological knowledge of the human's emotional brain. In this paper, ENNs are developed and examined for prediction tasks. Genetic algorithm is applied for optimal tuning of crisp numerical parameters of ENN. The performance of the proposed ENN is examined using data sets for a couple of synthetic (with constant and variable noise) and real world (wind farm power generation data) case studies. A traditional artificial neural network (ANN) is also implemented for comparison purposes. Numerical results indicate the superiority of ENN over ANN in terms of accuracy and stability.

We address a framework for the analysis of extended fuzzy logic (FLe) and elaborate mainly the key characteristics of FLe by proving several qualification theorems and proposing a new mathematical tool named the A-granule. Specifically, we reveal that within FLe a solution in the presence of incomplete information approaches the one gained by complete information. It is also proved that the answers and their validities have a structural isomorphism within the same context. This relationship is then used to prove the representation theorem that addresses the rationality of FLe-based reasoning. As a consequence of the developed theoretical description of FLe, we assert that in order to solve a problem, having complete information is not a critical need; however, with more information, the answers achieved become more specific. Furthermore, reasoning based on FLe has the advantage of being computationally less expensive in the analysis of a given problem and is faster.

An algorithm is proposed for scheduling dependent tasks in time-varying heterogeneous multiprocessor systems, in which computational power and links between processors are allowed to change over time. Link contention is considered in the multiprocessor scheduling problem. A linear switching-state space-modeling paradigm is introduced to enable theoretical analysis from a system engineering perspective. Theoretical analysis of this model shows its robustness against changes in processing power and link failure. The proposed algorithm uses a fuzzy decision-making procedure to handle changes in the multiprocessor system. The efficiency of the proposed algorithm is illustrated by several random experiments and comparison against a recent benchmark approach. The results show up to 18% average improvement in makespan, especially for larger scale systems.

The original version of this article unfortunately contained a mistake. The affiliation of the first author was incorrect. The correct affiliation is: Department of Computer Engineering, Quchan Branch, Islamic Azad University, Quchan, Iran. Journal of Zhejiang University-SCIENCE C (Computers & Electronics) ISSN 1869-1951 (Print); ISSN 1869-196X (Online) www.zju.edu.cn/jzus; www.springerlink.com E-mail: [email protected]

A new confabulation-inspired association rule mining (CARM) algorithm is proposed using an interestingness measure inspired by cogency. Cogency is only computed based on pairwise item conditional probability, so the proposed algorithm mines association rules by only one pass through the file. The proposed algorithm is also more efficient for dealing with infrequent items due to its cogency-inspired approach. The problem of associative classification is used here for evaluating the proposed algorithm. We evaluate CARM over both synthetic and real benchmark data sets obtained from the UC Irvine machine learning repository. Experiments show that the proposed algorithm is consistently faster due to its one time file access and consumes less memory space than the Conditional Frequent Patterns growth algorithm. In addition, statistical analysis reveals the superiority of the approach for classifying minority classes in unbalanced data sets.

In this paper, a direct adaptive interval type-2 fuzzy sliding mode control scheme is proposed for synchronizing two different chaotic systems. This method guarantees the global asymptotic stability synchronization of the two state trajectories using lyapunov stability criteria. This combination reduced the chattering phenomena of the control efforts by type-2 fuzzy paradigm. To elucidate the performance of this method, the simulation results to synchronize two different chaotic systems such as Lorenz and Chen are given. This method ensures the convergence of the synchronization error between two systems towards zero as time goes to infinity. Simulation results show the effectiveness of proposed method.

In this paper, we propose a Fuzzy Adaptive Brain-Inspired Emotional Decayed Learning named Fuzzy ADBEL. Fuzzy ADBEL is a computational model that models the forgetting process and inhibitory mechanism of the emotional brain. In the model, the fuzzy decay rate simulates the forgetting process, and the stimulus and learning weights are considered as fuzzy variables trained by fuzzy learning rules. The final output of the model is evaluated by a fuzzy decision making layer that simulates the inhibitory mechanism. The proposed Fuzzy ADBEL is utilized to predict the Kp, AE and Dst indices showing opposite behaviors and characterizing the chaotic activity of the earth's magnetosphere. Experimental results show that fuzzy approaches including Fuzzy ADBEL and ANFIS (Adaptive NeuroFuzzy Inference System) reaches steady state faster than non-fuzzy approaches, ADBEL and MLP (Multilayer Perceptron). Hence, we hope the proposed model can be used in real time chaotic time series prediction.

Nowadays scalability and capability of parallel execution are the most important characteristics for data mining algorithms due to the growing size of data sets. In this paper, a new distributed framework called DFRM is proposed to extract fuzzy rules from numerical data using a multi-agent approach. These extracted rules can be used for classification and decision making tasks. Scalability, self-organization and uncertainty handling are important characteristics of the proposed system. Scalability and self-organization are provided by autonomous agents in the learning process. Interaction among agents can lead to a more compact fuzzy rule base for decision making. Moreover the training samples are split equally between the agents randomly. Therefore each agent has a partial view of data set. Four UCI data sets are used to evaluate the proposed framework based on accuracy and rule base size. Experimental results show that the resulting distributed classification model maintains acceptable accuracy with fewer rules In addition, this model is robust against non-availability of training data.

In this article, the brain emotional learning-based pattern recognizer (BELPR) is proposed to solve multiple input–multiple output classification and chaotic time series prediction problems. BELPR is based on an extended computational model of the human brain limbic system that consists of an emotional stimuli processor. The BELPR is model free and learns the patterns in a supervised manner and evaluates the output(s) using the activation function tansig. In the numerical studies, various comparisons are made between BELPR and a multilayer perceptron (MLP) with a back-propagation learning algorithm. The methods are tested to classify 12 UCI (University of California, Irvine) machine learning data sets and to predict activity indices of the Earth's magnetosphere. The main features of BELPR are higher accuracy, decreased time and spatial complexity, and faster training.

Artificial fish swarm algorithm is one of the swarm intelligence algorithms which performs based on population and stochastic search contributed to solve optimization problems. This algorithm has been applied in various applications e.g. data clustering, neural networks learning, nonlinear function optimization, etc. Several problems in real world are dynamic and uncertain, which could not be solved in a similar manner of static problems. In this paper, for the first time, a modified artificial fish swarm algorithm is proposed in consideration of dynamic environments optimization. The results of the proposed approach were evaluated using moving peak benchmarks, which are known as the best metric for evaluating dynamic environments, and also were compared with results of several state-of-the-art approaches. The experimental results show that the performance of the proposed method outperforms that of other algorithms in this domain.

To date, various paradigms of soft-Computing have been used to solve many modern problems. Among them, a self organizing combination of fuzzy systems and neural networks can make a powerful decision making system. Here, a Dynamic Growing Fuzzy Neural Controller (DGFNC) is combined with an adaptive strategy and applied to a 3PSP parallel robot position control problem. Specifically, the dynamic growing mechanism is considered in more detail. In contrast to other self-organizing methods, DGFNC adds new rules more conservatively; hence the pruning mechanism is omitted. Instead, the adaptive strategy ‘adapts’ the control system to parameter variation. Furthermore, a sliding mode-based nonlinear controller ensures system stability. The resulting general control strategy aims to achieve faster response with less computation while maintaining overall stability. Finally, the 3PSP is chosen due to its complex dynamics and the utility of such approaches in modern industrial systems. Several simulations support the merits of the proposed DGFNC strategy as applied to the 3PSP robot.

In this paper we propose the supervised version of neuro-based computational model of brain emotional learning (BEL). In mammalian brain, the limbic system processes emotional stimulus and consists of following two main components: amygdala and orbitofrontal cortex (OFC). Recently several models of BEL based on monotonic reinforcement learning in amygdala are proposed by researchers. Here, we introduce supervised version of BEL which can be learned by pattern-target examples. According to the experimental studies, where various comparisons are made between the proposed method, multilayer perceptron (MLP) and adaptive neuro-fuzzy inference system (ANFIS), the main feature of the presented method is fast training in prediction problems.

In this study, a novel approach is proposed for mental stress recognition through automatic analysis of eye video sequences. The proposed system consists of five stages: video capturing, fuzzy image processing, signal processing, feature extraction and, classification. The pupil parameters including Pupil Diameter (PD) and Pupil Dilation Acceleration (PDA) are measured using soft computing techniques wherein the eye region is detected using the genetic algorithm (GA), and a fuzzy filter is designed for noise reduction. Edge detection is performed based on fuzzy reasoning and linking is done using Hough transform. Then, signal processing technique is applied to the pupil parameters to extract their most relevant features. Extracted features are imported into the learning system to classify the affective states between "stress" and "relaxed". The Fuzzy SVM (FSVM) is applied to this classification process. In order to induce the stress in subjects, a Stroop color-word test is designed. Also, the results obtained from the pupil parameters are compared with two other physiological signals including Electrocardiogram (ECG) and Photoplethysmogram (PPG). The experimental results indicate the pupil parameters have great potential for stress recognition compared to the other two physiological signals and, proposed stress recognition system is promising.

Particle Swarm Optimization (PSO) has received great attention in recent years as a successful global search algorithm, due to its simple implementation and inexpensive computation overhead. However, PSO still suffers from the problem of early convergence to locally optimal solutions. Extremal Optimization (EO) is a local search algorithm that has been able to solve NP hard optimization problems. The combination of PSO with EO benefits from the exploration ability of PSO and the exploitation ability of EO, and reduces the probability of early trapping in the local optima. In other words, due to the EO's strong local search capability, the PSO focuses on its global search by a new mutation operator that prevents loss of variety among the particles. This is done when the particle's parameters exceed the problem conditions. The resulting hybrid algorithm Mutated PSO-EO (MPSO-EO) is then applied to the Traveling Salesman Problem (TSP) as a NP hard multimodal optimization problem. The performance of the proposed approach is compared with several other metaheuristic methods on 3 well known TSP databases and 10 unimodal and multimodal benchmark functions.

This paper presents a facial expression recognition approach to recognize the affective states. Feature extraction is a vital step in the recognition of facial expressions. In this work, a novel facial feature extraction method based on Intersecting Cortical Model (ICM) is proposed. The ICM network which is a simplified model of Pulse-Coupled Neural Network (PCNN) model has great potential to perform pixel grouping. In the proposed method the normalized face image is segmented into two regions including mouth, eyes using fuzzy c-means clustering (FCM). Segmented face images are imported into an ICM network with 300 iteration number and pulse image produced by the ICM network is chosen as the face code, then the support vector machine (SVM) is trained for discrimination of different expressions to distinguish the different affective states. In order to evaluate the performance of the proposed algorithm, the face image dataset is constructed and the proposed algorithm is used to classify seven basic expressions including happiness, sadness, fear, anger, surprise and hate The experimental results confirm that ICM network has great potential for facial feature extraction and the proposed method for human affective recognition is promising. Fast feature extraction is the most advantage of this method which can be useful for real world application.

The World Wide Web has created a new media for mass marketing that can also be highly customized to online customers' needs and expectations. Recommender Systems (RS) play an important role in this area. Here, we aim to establish a genre-based collaborative RS to automatically suggest and rank a list of appropriate items (movies) to a user based on the user profile and the past voting patterns of other users with similar tastes. The contribution of this paper is using genre based information in a hybrid fuzzy-Bayesian network collaborative RS. The interest to the different genres is computed based on a hybrid user model. The similarity of like-minded users according to the fuzzy distance and also Pearson correlation coefficient is involved in a Bayesian network.

Nowadays, wireless sensor networks are widely used in a variety of applications such as in military, vehicle tracking, disaster management and environmental monitoring. Accurate estimation of the sensor position can be crucial in many of these applications. In this research, we propose a localization algorithm based on probabilistic fuzzy logic systems (PFLS) for range-free localization. The algorithm utilizes received signal strength (RSS) from the anchor nodes embedded with variant degrees of environmental noise. The proposed system is compared with another algorithm based on Fuzzy Logic Systems (FLS) against variant amount of noise. Simulation results demonstrate that FLS can be much more accurate than PFLS method if the environment is noise-free. However, as the environmental noise increases, the PFLS reaches better performance.

Fractal image compression explores the self-similarity property of a natural image and utilizes the partitioned iterated function system (PIFS) to encode it. This technique is of great interest both in theory and application. However, it is time-consuming in the encoding process and such drawback renders it impractical for real time applications. The time is mainly spent on the search for the best-match block in a large domain pool. In order to solve the high complexity of the conventional encoding scheme for fractal image compression, a spatial correlation chaotic particle swarm optimization (SC-CPSO), based on the characteristics of fractal and partitioned iterated function system (PIFS) is proposed in this paper. There are two stages for the algorithm: (1) Make use of spatial correlation in images for both range and domain pool to exploit local optima. (2) Adopt chaotic PSO (CPSO) to explore the global optima if the local optima are not satisfied. Experiment results show that the algorithm convergent rapidly. At the premise of good quality of the reconstructed image, the algorithm saved the encoding time and obtained high compression ratio.

Human sleep is divided into two segments, Rapid Eye Movement (REM) sleep and Non-REM (NREM) sleep. NREM sleep is further divided into 4 stages. Sleep staging attempts to identify these stages based on the signals collected in PSG. Significant information can be derived from the EEG signals collected during PSG. Wavelet coefficients are extracted from EEG signals. In order to reduce the amount of data set, the statistical features are calculated from wavelet coefficients. For performing decision making, six ANFIS classifiers and SVM classifier are used to differentiate between REM and Non-REM sleep stages. That is to say, pattern varies under the different sleep stages. Therefore, healthy humans with a regular night's sleep will follow these sleep stages in a particular pattern.

Estimating the head pose plays an important role in computer vision and also as a key task for visual surveillance and face recognition applications hence a prominent problem in computer vision. Most of the works in this field suffer from lack of continuous estimating of the head pose and high accuracy. We know fuzzy systems as universal approximator capable of approximating an unknown function by having just few limited information while attaining high accuracy. In this paper, an improved approach is proposed for estimating the rotation angle of the head along horizontal axis based on a fuzzy approximator in which the membership functions are of Gaussian type. The proposed method is able to provide a continuous estimate of the head along horizontal axis with high accuracy, low computational cost while avoiding from getting involved into complex mathematical equations. Experiments on images from two standard well-known databases showed less than 6° of average absolute error in estimation which is a significant improvement over the approximator with simple triangular membership functions.

Computational complexity is a major challenge in evolutionary algorithms due to their need for repeated fitness function evaluations. In the context of multiobjective evolutionary algorithms, there are a few attentions to the computational complexity of this kind of algorithms. Here, we aim to reduce number of fitness function evaluations in multiobjective cellular genetic algorithms by the use of fitness granulation via an adaptive fuzzy similarity analysis. In the proposed algorithm, an individual's fitness is only computed if it has insufficient similarity to a queue of fuzzy granules whose fitness has already been computed. If an individual is sufficiently similar to a known fuzzy granule, then that granule's fitness is used instead as a crude estimate. Otherwise, that individual is added to the queue as a new fuzzy granule. The queue size as well as each granule's radius of influence is adaptive and will grow/shrink depending on the population fitness and the number of dissimilar granules. The proposed method is applied to a set of 6 test problems. In comparison with two well-known multiobjective evolutionary algorithms, NSGA-II, and MoCell, computational results show that the proposed method is competitive with these algorithms.

The Vehicle Routing Problem (VRP) is an NP-hard combinatorial optimization problem, seeking to serve a number of customers with a fleet of available vehicles. VRP is an important optimization problem in the field of transportation, distribution and logistics. To date, several exact and approximate approaches have been proposed to solve VRP. Here, we apply a population based algorithm to VRP by imitating the natural flow of water drops. The “Intelligent Water Drops” or IWD algorithm solves the VRP by modeling how water drops collectively modify their environment by picking up dirt from river bottoms during moving downhill and leaving sediments (such as on beaches) when slowing down. The computational results for fourteen benchmark VRP problems are reported and compared to several other metaheuristic approaches.

Human perception and processing of information is granular and multi-resolution instead of numerical and precise. Due to this multi-resolution perception-based computing, human mind can quickly evaluate (calculate) the fitness of a large subspace of the search space. Indeed, this characteristic enables human to simplify and solve very complex problems. In contrast, evolutionary optimization (EO) as one of the most applied artificial problem solvers is based on computing with numbers since a chromosome is a single point of the search space and fitness function calculation is numerical. Hence, EO is blind towards the optimization landscape and this blindness inhibits its performance when the search space is very large and complex. Inspired by human perception based reasoning, a novel approach to optimization and problem solving is proposed here. Perception-based evolutionary optimization (PEO) is fundamentally based on computing with words. In PEO, chromosomes and fitness function calculation are perception-based (granular) instead of numerical and thus PEO works with granules (subspaces) rather than single points. Also, search is performed in a multi-resolution manner.

Spin glass optimization is a distributed technique inspired by the interactions in spin glasses in nature. Spin glasses are the lattices of spins where each spin is only a part of the entire solution, in contrast to genetic algorithms (GAs), where each chromosome represents a complete solution. The interaction between spins creates special optimal patterns given appropriate temperature. This optimization paradigm is promising in complex multiobjective optimization tasks because it allows high-computational parallelism among its member spins. Furthermore, since the overall network of spins represents only one solution, there is a great promise in computational efficiency when compared with other population-based/stochastic approaches such as GAs and simulated annealing. The nature of this method is also entirely different from other distributed frameworks such as Hopfield neural network since spins' paradigm of interaction does not have to be fully connected; i.e., the neighborhoods of interactions can expand or collapse, hence less computation and better convergence speed. In this paper, we apply a heuristic method based on the spin glass model that uses migration and elitism operators in addition to temperature control in order to trace out an efficient frontier in the optimization landscape. The proposed methodology is then applied to the problem of portfolio selection. Portfolio selection is one of the nondeterministic polynomial complete problems where each asset's behavior is similar to spin's behavior and it is therefore suitable as a case study. We show that, in proper circumstances, decrementing local energy of each spin can decrement global energy of the glass, and correspondingly, if the optimization problem can be suitably mapped to the glass, the expected cost function decreases.

The need for greater capacity in automotive transportation (in the midst of constrained resources) and the convergence of key technologies from multiple domains may eventually produce the emergence of a "swarm" concept of operations. The swarm, which is a collection of vehicles traveling at high speeds and in close proximity, will require technology and management techniques to ensure safe, efficient, and reliable vehicle interactions. We propose a shared autonomy control approach, in which the strengths of both human drivers and machines are employed in concert for this management. Building from a fuzzy logic control implementation, optimal architectures for shared autonomy addressing differing classes of drivers (represented by the driver's response time) are developed through a genetic-algorithm-based search for preferred fuzzy rules. Additionally, a form of "phase transition" from a safe to an unsafe swarm architecture as the amount of sensor capability is varied uncovers key insights on the required technology to enable successful shared autonomy for swarm operations.

Intrusion detection has become an indispensable defense line in the information security infrastructure. The existing signature-based intrusion detection mechanisms are often not sufficient in detecting many types of attacks. K-means is a popular anomaly intrusion detection method to classify unlabeled data into different categories. However, it suffers from the local convergence and high false alarms. In this paper, two soft computing techniques, fuzzy logic and swarm intelligence, are used to solve these problems. We proposed SFK-means approach which inherits the advantages of K-means, Fuzzy K-means and Swarm K-means, simultaneously we improve the deficiencies. The most advantages of our SFK-means algorithm are solving the local convergence problem in Fuzzy Kmeans and the sharp boundary problem in Swarm Kmeans. The experimental results on dataset KDDCup99 show that our proposed method can be effective in detecting various attacks.

This paper presents a multi-agent framework for distributed web-based face recognition using fuzzy logic based result fusion mechanism. The model is designed in JADE framework and takes advantage of JXTA agent communication method to allow agent communication through firewalls and network address translators. This approach enables police stations to build and deploy P2P applications through a unified medium to manage agent-based criminal face image retrieval from multiple distributed databases over the Internet.

Various forms of swarm intelligence are inspired by social behavior of insects that live collectively. AntNet is a form of such social algorithms, but it has a scalability problem with growing network size. If every node sends only one ant to each destination node and there are N nodes in the network, the total number of ants that are sent is N(N-1). In addition with increasing overhead for large networks, most of the ants are often lost for distant destinations. Furthermore, due to long travel times, ants that do arrive may carry outdated information. In this paper, a novel hierarchical algorithm is proposed to resolve this scalability problem of AntNet. The proposed Super-AntNet divides a large scale network into several small networks that are chosen based their internal traffic patterns. A separate ant colony is then assigned to each of these networks. A Super-Ant Colony is then responsible to coordinate data routing among the colonies. Performance of Super-AntNet is compared with those of standard AntNet as well as two other conventional routing algorithms such as Distance Vector (DV) and Link State (LS) in terms of end-to-end delay, throughput, packet loss ratio, increased overhead, as well as jitter. Application to a 16-node network indicates the superiority of the proposed algorithm.

Much of the computational complexity in employing evolutionary algorithms as optimization tool is due to the fitness function evaluation that may either not exist or be computationally very expensive. With the proposed approach, the expensive fitness evaluation step is replaced by an approximate model. An intelligent guided technique via an adaptive fuzzy similarity analysis for fitness granulation is used to decide on use of expensive function evaluation and dynamically adapt the predicted model. In order to avoid tuning parameters in this approach, a fuzzy supervisor as auto-tuning algorithm is employed with three inputs. The proposed method is then applied to three traditional optimization benchmarks with four different choices for the dimensionality of the search apace. Effect of number of granules on rate of convergence is also studied. In comparison with standard application of evolutionary algorithms, statistical analysis confirms that the proposed approach demonstrates an ability to reduce the computational complexity of the design problem without sacrificing performance. Furthermore, the auto-tuning of the fuzzy supervisory removes the need for exact parameter determination.

A novel optimization algorithm is proposed here that is inspired by the principles of magnetic field theory. In the proposed Magnetic Optimization Algorithm (MOA) the possible solutions are magnetic particles scattered in the search space. Each magnetic particle has a measure of mass and magnetic field according to its fitness. The fitter magnetic particles are those with higher magnetic field and higher mass. These particles are located in a lattice-like environment and apply a force of attraction to their neighbors. The proposed cellular structure allows a better exploitation of local neighborhoods before they move towards the global best, hence it increases population diversity. Experimental results on 14 numerical benchmark functions show that MOA in some benchmark functions can work better than GA and PSO.

A diversity preserving cellular quantum evolutionary algorithm (DPCQEA) is proposed in which the quantum individuals are located in a specific topology and interact only with their neighbors. The proposed cellular structure aims to provide a better exploitation of local neighborhoods before moving towards a global best, hence it increases population diversity. This paper also proposes a new operator for diversity preservation in the population. In standard QEA the diversity in the population decreases across the generations. Decreasing the diversity of the population decreases the exploration performance of the algorithm and causes possible algorithm trapping in the local optima. In the proposed algorithm, only the fittest of converged q-individuals from among similar individuals are preserved, while others are reinitialized. A criterion is then proposed to measure convergence and similarity among individuals. Experimental results on knapsack problem, trap problem as well as 14 Numerical benchmark functions show that DPCQEA consistently exceeds the performance of QEA.

Design of controllers for uncertain systems is inherently paradoxical. Adaptive control approaches claim to adapt system parameters against uncertainties, but only if these uncertainties change slowly enough. Alternatively, robust control methodologies claim to ensure system stability against uncertainties, but only if these uncertainties remain within known bounds. This is while, in reality, disturbances and uncertainties remain faithfully uncertain, i.e., may be both fast and large. In this paper, a PI-adaptive fuzzy control architecture for a class of uncertain nonlinear systems is proposed that aims to provide added robustness in the presence of large and fast but bounded uncertainties and disturbances. While the proposed approach requires the uncertainties to be bounded, it does not require this bound to be known. Lyapunov analysis is used to prove asymptotic stability of the proposed approach. Application of the proposed method to a second-order inverted pendulum system demonstrates the effectiveness of the proposed approach. Specifically, system responses to fast versus slow and large versus small disturbances are considered in the presented simulation studies.

Computational complexity is a major challenge in evolutionary algorithms due to their need for repeated fitness function evaluations. Here, we aim to reduce number of fitness function evaluations by the use of fitness granulation via an adaptive fuzzy similarity analysis. In the proposed algorithm, an individual’s fitness is only computed if it has insufficient similarity to a queue of fuzzy granules whose fitness has already been computed. If an individual is sufficiently similar to a known fuzzy granule, then that granule’s fitness is used instead as a crude estimate. Otherwise, that individual is added to the queue as a new fuzzy granule. The queue size as well as each granule’s radius of influence is adaptive and will grow/shrink depending on the population fitness and the number of dissimilar granules. The proposed technique is applied to a set of 6 traditional optimization benchmarks that are for their various characteristics. In comparison with standard application of evolutionary algorithms, statistical analysis reveals that the proposed method will significantly decrease the number of fitness function evaluations while finding equally good or better solutions.

In this paper we propose a neuro-fuzzy architecture for Web content taxonomy using hybrid of Adaptive Resonance Theory (ART) neural networks and fuzzy logic concept. The search engine called Kavosh1 is equipped with unsupervised neural networks for dynamic data clustering. This model was designed for retrieving images without metadata and in estimating resemblance of multimedia documents; however, in this work only text mining method is implemented. Results show noticeable average precision and recall over search results.

The need for greater capacity in automotive transportation (in the midst of constrained resources) and the convergence of key technologies from multiple domains may eventually produce the emergence of a “swarm” concept of operations. The swarm, a collection of vehicles traveling at high speeds and in close proximity, will require management techniques to ensure safe, efficient, and reliable vehicle interactions. We propose a shared-autonomy approach in which the strengths of both human drivers and machines are employed in concert for this management. Inspired by prior research, a fuzzy logic-based control implementation is developed and described in this paper for the purpose of exploring the shared-autonomy concept.

In this paper, a novel constructive-optimizer neural network (CONN) is proposed for the traveling salesman problem (TSP). CONN uses a feedback structure similar to Hopfield-type neural networks and a competitive training algorithm similar to the Kohonen-type self-organizing maps (K-SOMs). Consequently, CONN is composed of a constructive part, which grows the tour and an optimizer part to optimize it. In the training algorithm, an initial tour is created first and introduced to CONN. Then, it is trained in the constructive phase for adding a number of cities to the tour. Next, the training algorithm switches to the optimizer phase for optimizing the current tour by displacing the tour cities. After convergence in this phase, the training algorithm switches to the constructive phase anew and is continued until all cities are added to the tour. Furthermore, we investigate a relationship between the number of TSP cities and the number of cities to be added in each constructive phase. CONN was tested on nine sets of benchmark TSPs from TSPLIB to demonstrate its performance and efficiency. It performed better than several typical Neural networks (NNs), including KNIES_TSP_Local, KNIES_TSP_Global, Budinich's SOM, Co-Adaptive Net, and multivalued Hopfield network as wall as computationally comparable variants of the simulated annealing algorithm, in terms of both CPU time and accuracy. Furthermore, CONN converged considerably faster than expanding SOM and evolved integrated SOM and generated shorter tours compared to KNIES_DECOMPOSE. Although CONN is not yet comparable in terms of accuracy with some sophisticated computationally intensive algorithms, it converges significantly faster than they do. Generally speaking, CONN provides the best compromise between CPU time and accuracy among currently reported NNs for TSP.

This paper presents genetic quantum algorithm and its associated evolutionary tools for the voltage and pattern design of piezoelectric actuator. Genetic quantum algorithms (GQA) is similar to genetic algorithms (GA) maintain a population of individuals but each individual is composed of probabilistic quantum bits for preserving diversity. Also, instead of crossover or mutation, GQA use quantum gates (QG) to update individuals and to guide the evolutionary process. Genetic quantum algorithm can be use as an effective optimization tool for shape control of structures. Shape control of structures, with special emphasis on piezoelectric control actuation has found an wide range of applications in recent years. In shape control one intends to specify the spatial distribution, or the shape, of an actuating control agency, such that the displacement field of a structure is distorted from its original shape.

Variable Bit Rate (VBR) video traffic modeling is one of the most important challenges in setting up the communication networks. It is applicable to the efficient designing and using of the networks. There are some solutions to the problem of VBR Video traffic modeling. These solutions are divided in to two categories: statistical modeling and deterministic modeling. In this paper, we propose a new deterministic model based on the nonlinear mapping characteristic of fuzzy systems, implemented using a Generalized Regression Neural Network (GRNN). This model is tested with prediction of next 5, 10 and 20 frames of I, P and B frames of the desired movie, using 100 current frames. The model generated data is examined by the matching of autocorrelation function and histogram. This model can regenerate the ACF and histogram of video traces, in order of 10-3in error.

Two genetic quantum-based algorithms are proposed for large scale problem analysis, and are compared with elite GA and a hybrid algorithm of GA and neural network (NN) in terms of computational efficiency with equal or better performance. Genetic quantum algorithms (GQA) similar to genetic algorithms (GA) maintain a population of individuals but each individual is composed of probabilistic quantum bits for preserving diversity, i.e. each individual of length m is equivalent to 2/sup m/ states. Also instead of crossover or mutation, GQA use quantum gates (QG) to update individuals and to guide the evolutionary process. The role of NN is to replace the time consuming state of finite element analysis, but the neuro-approximation introduces error in fitness prediction as well. In comparison, statistical analysis reveals that GQA is not only relatively simpler, but it also decreases optimization time while finding better solutions. Furthermore, the usage of neural networks helps further reduce finite element evaluations without significantly compromising quality of solutions.

A stable direct adaptive fuzzy PI sliding mode controller for a class of nonlinear uncertain systems is investigated. The proposed method is robust in the presence of uncertainties and bounded external disturbances. Considering other existing approaches of handling external disturbances, the proposed approach does not need a bound to be known; only requiring that it exists. Moreover, the PI control structure is used for attenuating chattering problem. The approach is applied to a nonlinear system as well as a chaotic nonlinear system via significant disturbances. Analysis of simulations reveals the effectiveness of the proposed method.

Genetic quantum algorithms (GQA) are population based evolutionary algorithms that imitate quantum physics by introducing quantum bits for a basic probabilistic genotypic representation and hence better population diversity, and quantum gates for evolving the population of solutions. While quantum inspired gates play an important role in the evolutionary process, there is no specific method for their design, i.e. they are mostly developed through ad hoc procedures. Here, we propose a multi-objective meta-level GQA in order to determine parameters of QG which is applicable for a wide variety of optimization problems. Specifically, a two-layer GQA is constructed, in which the lower layer's objective is to optimize the four junction types: Dejong, Peak, Easoms, and Griewank. And the higher layer's objective is to determine optimal parameters for QG that helps the proposed algorithm find best solutions. GQA optimization performance, with optimized parameter of QG is compared with GA on several benchmark problems and the superiority of the proposed method is statistically shown.

A robust PI adaptive fuzzy controller for a class of uncertain nonlinear systems is investigated. The proposed method is robust in the presence of uncertainties and bounded external disturbances. To cope with the lack of system structure and signal uncertainty the adaptive fuzzy control is used. Furthermore, a PI-type control is introduced to cope with the bounded disturbances, which also provides robustness in the presence of large disturbances. Asymptotic stability of the proposed method is shown based on Lyapunov approach. Application of the proposed method to an inverted pendulum system demonstrates the effectiveness of the proposed approach.

In this paper, we present the use of stochastic learning automata (SLA) in multiagent robotics. In order to fully utilize and implement learning control algorithms in the control of multiagent robotics, an environment for simulation has to be first created. A virtual laboratory for simulation of autonomous agents, called V-Lab is described. The V-Lab architecture can incorporate various models of the environment as well as the agent being trained. A case study to demonstrate the use of SLA is presented.

A modified table look-up scheme for fuzzy systems is applied to learn and imitate human control strategy in tracking control of an overhead crane. Applications of modeling human behavior are not only designing controllers based on human action, but also learning human behavior for studying it and predicting its response. Learning human behavior in itself is not a new research issue; however, when paying particular attention to its random nature, it is still a challenging problem. Consequently, we focus here on fuzzy learning with a consideration for random nature of human behavior. A computer simulator is developed here for manual control of the crane and the simulation results after learning human control strategy confirm that the new modified method performs better than standard learning method.

A concept of probabilistic fuzzy logic is introduced as a way of representing and/or modeling existing randomness in many real world systems and natural language propositions. The approach is based on combining both the concepts of probability of truth and degree of truth in a unique framework. This combination is carried out in both fuzzy sets and fuzzy rules resulting in the concepts of probabilistic fuzzy sets and probabilistic fuzzy rules, respectively. Having one of these probabilistic elements, a probabilistic fuzzy system is then introduced as a fuzzy-probabilistic model of a complex nondeterministic system. In a simple example, human skepticism about the optimal fuzzy rule base is modeled through substituting a probabilistic fuzzy rule base for a conventional one. The closed loop response of the resulting controller for tank level control is shown through simulation and is compared with a conventional fuzzy controller.

A novel approach based on soft computing concepts is proposed for fault detection and isolation (FDI) of dynamic systems. The proposed method utilizes the concepts of fuzzy clustering, fuzzy decision making and RBF neural networks to create a suitable structure for design of a FDI. The practical applicability is illustrated on a CNC X-axis drive system. Specifically, FDI of twelve different process faults and three different sensor faults is successfully detected for the CNC system.

In recent years, evolution-based knowledge optimization has gained a great deal of popularity due to its inherent ability in efficient and parallel search of complex and multimodal landscapes. Application of genetic algorithms (GA) to knowledge enhancement involves several aspects. First is how to code a string to represent all the necessary degrees of freedom for search in the fuzzy knowledge domain. The second aspect is how to incorporate existing expert knowledge into the GA-optimising algorithm. And in general, how to take advantage of several experts' opinions in creation of an initial population. Conventional applications of GA-fuzzy suggest using a random initial population. However, it is intuitively clear that any search routine could converge faster if starting points are good solutions. In this paper, a methodology is illustrated which incorporates expert knowledge in creating an initial population while allowing for randomness among members of the population for diversity. Furthermore, the methodology is applied to step response optimization of a flexible-link feedback control system.