The intelligent devices in Internet of Things (IoT) not only provide services but also consider how to allocate heterogeneous resources and reduce resource consumption and service time as far as possible. This issue becomes crucial in the case of large-scale IoT environments. In order for the IoT service system to respond to multiple requests simultaneously and provide Pareto optimal decisions, we propose an immune-endocrine system inspired hierarchical coevolutionary multiobjective optimization algorithm (IE-HCMOA) in this paper. In IE-HCMOA, a multiobjective immune algorithm based on global ranking with vaccine is designed to choose superior antibodies. Meanwhile, we adopt clustering in top population to make the operations more directional and purposeful and realize self-adaptive searching. And we use the human forgetting memory mechanism to design two-level memory storage for the choice problem of solutions to achieve promising performance. In order to validate the practicability and effectiveness of IE-HCMOA, we apply it to the field of agricultural IoT service. The simulation results demonstrate that the proposed algorithm can obtain the best Pareto, the strongest exploration ability, and excellent performance than nondominated neighbor immune algorithms and NSGA-II.

Multiple solutions are often needed because of different kinds of uncertain failures in a plan execution process and scenarios for which precise mathematical models and constraints are difficult to obtain. This paper proposes an optimization strategy for multirobot task allocation (MRTA) problems and makes efforts on offering multiple solutions with same or similar quality for switching and selection. Since the mentioned problem can be regarded as a multimodal optimization one, this paper presents a niching immune-based optimization algorithm based on Softmax regression (sNIOA) to handle it. A prejudgment of population is done before entering an evaluation process to reduce the evaluation time and to avoid unnecessary computation. Furthermore, a guiding mutation (GM) operator inspired by the base pair in theory of gene mutation is introduced into sNIOA to strengthen its search ability. When a certain gene mutates, the others in the same gene group are more likely to mutate with a higher probability. Experimental results show the improvement of sNIOA on the aspect of accelerating computation speed with comparison to other heuristic algorithms. They also show the effectiveness of the proposed GM operator by comparing sNIOA with and without it. Two MRTA application cases are tested finally.

Recently, deep learning has been widely applied in various areas and achieved remarkable research findings. The major reason that makes the deep learning paradigm successful is that it can effectively learn a hierarchical feature structure for the training data. However, most deep learning algorithms rely on massive well-labeled training datasets and hyper-parameter configurations. This paper proposed a novel methodology that uses the geometric characteristics of line-segment representations to optimize the hyper-parameters for the deep networks. The methodology is applied to a line-segment-based stacked auto-encoder to verify its effectiveness. It is found that the line-segment-based visualizations can increase the interpretability of the deep models and facilitate the configurations for the hyper-parameters.

Today, in the construction of smart city, the development of self-driving technology plays the key role. The explosion of convolutional neural network (CNN) technology has made it possible to utilize end-to-end tasks with images. However, today’s CNN has deeper, more accurate characteristics. If we do not improve the calculation method to reduce the number of network parameters, this feature makes it very difficult for us to run neural network computing in small devices. In this paper, we further optimize the network computing methods based on MobileNets to reduce number of network parameters. At the same time, in the network structure, we add BatchNormalization and Swish activation function. We designed our own network in the end-to-end prediction for steering angle in the self-driving car task. From the final simulation results, our neural network’s storage space can be reduced and the execution speed of neural network can be improved while maintaining the accuracy of the neural network.

In this paper, a computing speed improvement for the clonal selection algorithm (CSA) is proposed based on a degeneration recognizing (DR) method. The degeneration recognizing clonal selection algorithm (DR-CSA) is designed for solving complex engineering multimodal optimization problems. On each iteration of CSA, there is a large amount of eliminated solutions which are usually neglected. But these solutions do contain the knowledge of the nonoptimal area. By storing and utilizing these data, the DR-CSA is aimed to identify part of the new population as degenerated and eliminate them before the evaluation operation, so that a number of evaluation times can be avoided. This pre-elimination operation is able to save computing time because the evaluation is the main reason for the time cost in the complex engineering optimization problem. Experiments on both test function and a real-world engineering optimization problem (wet spinning coagulating process) are conducted. The results show that the proposed DR-CSA is as accurate as regular CSA and is effective in reducing a considerable amount of computing time.

This paper proposes an augmented model approach for identification of nonlinear errors-in-variables (EIVs) systems. An EIV model accounts for uncertainties in the observations of both inputs and outputs. As the direct identification of nonlinear functions is difficult, we propose to approximate the nonlinear EIV model using multiple ARX models. To estimate the noise-free input signal, we use a collection of particle filters which run in parallel corresponding to each of the multiple ARX models. The parameters of local models are estimated by applying expectation maximization algorithm, under a maximum likelihood framework, using the input-output data of the nonlinear EIV system. Simulated numerical examples and an experiment study on a multitank system are used to illustrate the efficacy of the proposed approach.

Rolling element bearings play an important role in ensuring the availability of industrial machines. Unexpected bearing failures in such machines during field operation can lead to machine breakdown, which may have some pretty severe implications. However, the insufficiency of labeled samples is major problem for handling fault diagnosis problem. To address such concern, we propose a semi-supervised method for diagnosing faulty bearings by utilizing unlabeled samples. The superiority of our algorithm has been validated by comparison with other state-of art methods based on a rolling element bearing data. The classification accuracy of bearing data show that our algorithm is able to recognize different bearing fault categories effectively. Thus, it can be considered as a promising method for fault diagnosis.

In multi-camera video tracking, the tracking scene and tracking-target appearance can become complex, and current tracking methods use entirely different databases and evaluation criteria. Herein, for the first time to our knowledge, we present a universally applicable template library updating approach for multi-camera human tracking called multi-state self-learning template library updating (RS-TLU), which can be applied in different multi-camera tracking algorithms. In RS-TLU, self-learning divides tracking results into three states, namely steady state, gradually changing state, and suddenly changing state, by using the similarity of objects with historical templates and instantaneous templates because every state requires a different decision strategy. Subsequently, the tracking results for each state are judged and learned with motion and occlusion information. Finally, the correct template is chosen in the robust template library. We investigate the effectiveness of the proposed method using three databases and 42 test videos, and calculate the number of false positives, false matches, and missing tracking targets. Experimental results demonstrate that, in comparison with the state-of-the-art algorithms for 15 complex scenes, our RS-TLU approach effectively improves the number of correct target templates and reduces the number of similar templates and error templates in the template library.

Major impediments in developing models based on the input-output data of an industrial process are the outliers in the output and uncertainties in the inputs. To address this problem, this article proposes a robust identification approach for nonlinear errors-in-variables systems. The t- distribution is employed to model the process data to account for the outliers through the adjustable degrees of freedom. Furthermore, we propose to approximate the nonlinear dynamics of the process using multiple local ARX models and combine them using a softmax function based weighting approach. To deal with parameter uncertainties, the identification problem is casted in the Bayesian framework and posterior distributions of the model parameters are estimated using the variational Bayesian approach, instead of point estimations. A numerical example of continuous fermenter as well as an experiment study on the multitank system is employed to demonstrate potential of the proposed method.

Resubmission and replication are two fundamental and widely recognized techniques in distributed computing systems for fault tolerance. The resubmission based strategy has an advantage in resource utilization, while the replication based strategy can reduce the task completed time in the context of fault. However, few researches take these two techniques together for fault-tolerant workflow scheduling, especially in Cloud systems. In this paper, we present a novel fault-tolerant workflow scheduling (ICFWS) algorithm for Cloud systems by combining the aforementioned two strategies together to play their respective advantages for fault tolerance while trying to meet the soft deadline of workflow. First, it divides the soft deadline of workflow into multiple sub-deadlines for all tasks. Then, it selects a reasonable fault-tolerant strategy and reserves suitable resource for each task by taking the imbalance sub-deadlines among tasks and on-demand resource provisioning of Cloud systems into consideration. Finally, an online scheduling and reservation adjustment scheme is designed to select a suitable resource for the task with resubmission strategy and adjust the sub-deadlines as well as fault-tolerant strategies of some unexecuted tasks during the task execution process, respectively. The proposed algorithm is evaluated on both real-world and randomly generated workflows. The results demonstrate that the ICFWS outperforms some well-known approaches on corresponding metrics.

In this paper, a dynamic leader selection process of a multirobot system with leader-follower strategies is studied in terms of formation control. A fuzzy inference system is employed to evaluate the status of robots by means of their states. Based on the status, an affection-based model is proposed to trigger a leader selection module. Followers send out unsatisfied signals when they are disappointed at the current leader. The abashment value of the leader changes with its own status as well as the number of unsatisfied signals received from its followers. When its abashment value goes beyond a given threshold, a leader reselection process is triggered. Moreover, a swap-greedy algorithm is proposed to approximate the optimal solution for confirming the leader-follower relationship, which can be described as a combinatorial optimization problem to minimize the total travel distance of all the robots. Extensive simulation results demonstrate that the proposed model can improve the probability of a robot team escaping from local extreme points significantly, and even in the case of leader failure, the team can reselect a leader autonomously and keep moving toward the target.

In order to address the influence caused by unstable and uneven harvested energy among sensor nodes for clustering routing in energy harvesting wireless sensor networks (EH-WSNs), a novel clustering routing algorithm (CREW) is proposed in this work. The CREW is composed by cluster building phase and data transmission phase. In cluster building phase, the CREW uses two new concepts, Network Gradient and Waiting Time for cluster head Competition, to divide the network into unequal clusters and select the cluster heads based on residual energy and energy gain of nodes, respectively. In data transmission phase, it adopts an adaptive inter-cluster communication mechanism, to sufficiently store and utilize the harvesting energy. Finally, to verify the effectiveness of the proposed CREW, a series of experiments are conducted and compared with other related clustering routings for the EH-WSNs. Simulation results manifest that the CREW can provide effective clustering routing for the EH-WSNs and highlight the better performance of the proposed approach than that of similar techniques.

A new approach to solving dynamic optimization problems (DOPs), called robust optimization over time (ROOT) has recently been suggested and a number of optimization algorithms for finding ROOT solutions have been developed. These algorithms typically rely on the predicted fitness in the new environment to determine the robustness of a solution, which may be considerably different from the true robustness due to the errors in fitness prediction. Consequently, performance comparison based on the predicted fitness may be also misleading. In this paper, we propose a new formulation of the performance measures for quantitatively evaluate the performance of optimization algorithms for finding robust solutions over time, assuming that the actual optimal solution of the problem is known. We empirically verify the proposed performance measures by analyzing the robust optimal solutions obtained in two recently reported ROOT evolutionary algorithms. Our results indicate that the proposed performance measures are able to provide an effective means for performance comparison of ROOT algorithms.

Since there is not a not a not a very very very suitable dynamical leader suitable dynamical leader suitable dynamical leader suitable dynamical leader suitable dynamical leader suitable dynamical leader suitable dynamical leader suitable dynamical leader suitable dynamical leader suitable dynamical leader selection model for selection model for selection model for selection model for selection model for selection model for selection model for selection model for formations control formations control formations control formations control formations control of the of the of the multi-robot system, we propose an affection-based dynamic leader selection strategy. A leader selection modular is used to switch the leader autonomously according to the cognized environmental statement based on two virtual affections, disappointment and abashment. The disappointment of followers will increase with the time spending in the awful scenario. When it exceeds a certain threshold, the followers will broadcast an unsatisfied signal. The abashment value of leader changes with the unsatisfied signals received from the followers and flutters with its own status at the same time. When the abashment value goes beyond the threshold, the leader re-selection process will be triggered. The abashment threshold is determined by the theoretic calculation. Plenty of simulation results show that the multi-robot system with dynamic leader-follower strategy has more probability of getting out of the woods, and also promote the survivability rate in the leader falling situation by re-selecting a leader autonomously.

A new global nonlinear predictor with a particle swarm-optimized interval support vector regression (PSO-ISVR) is proposed to address three issues (viz., kernel selection, model optimization, kernel method speed) encountered when applying SVR in the presence of large data sets. The novel prediction model can reduce the SVR computing overhead by dividing input space and adaptively selecting the optimized kernel functions to obtain optimal SVR parameter by PSO. To quantify the quality of the predictor, its generalization performance and execution speed are investigated based on statistical learning theory. In addition, experiments using synthetic data as well as the stock volume weighted average price are reported to demonstrate the effectiveness of the developed models. The experimental results show that the proposed PSO-ISVR predictor can improve the computational efficiency and the overall prediction accuracy compared with the results produced by the SVR and other regression methods. The proposed PSO-ISVR provides an important tool for nonlinear regression analysis of big data.

Most of the multivariable industrial production processes have latent failures on account of its complexity and uncertainty. This brings a great hidden threat to the control system's safety. Actuator fault is one of the commonest failures in control systems. This paper addresses a reconfigurable control system whose controller can be reconstructed when any actuator is faulty. Regard the control system as a biological immune system, an immune reconfigurable control system (IRCS) is designed. The IRCS includes an actuator monitor to imitate the immune surveillance mechanism, a decision module to imitate the immune response mechanism, a control strategy repository to imitate the immune memory mechanism and an adaptive learning module to imitate the immune learning mechanism. The IRCS is applied to the polyacrylonitrile carbon fiber coagulation bath, and the simulation results show that when there is any actuator fault, the IRCS can guarantee better performance compared with the conventional control schemes.

This paper studies on the predicted regression model of support vector machines (SVM). Aiming at the shortage that with the amount of samples grows, training time increases rapidly as well, we propose an optimization algorithm to optimize it for large scale dataset. The optimization algorithm is based on ensemble learning and dynamic clustering. Firstly, we use dynamic cluster method to generate different types of sub training set based on fuzzy granular. Then we construct SVM sub-learners. Afterwards we synthesize outputs of each sub-learner by using the strategy of mean squared error. Simulation experimental results demonstrate that the optimization algorithm can increase training speed obviously, and keep the original accuracy compared to traditional SVM.

A bidirectional optimizing approach for the melting spinning process based on an immune-enhanced neural network is proposed. The proposed bidirectional model can not only reveal the internal nonlinear relationship between the process configuration and the quality indices of the fibers as final product, but also provide a tool for engineers to develop new fiber products with expected quality specifications. A neural network is taken as the basis for the bidirectional model, and an immune component is introduced to enlarge the searching scope of the solution field so that the neural network has a larger possibility to find the appropriate and reasonable solution, and the error of prediction can therefore be eliminated. The proposed intelligent model can also help to determine what kind of process configuration should be made in order to produce satisfactory fiber products. To make the proposed model practical to the manufacturing, a software platform is developed. Simulation results show that the proposed model can eliminate the approximation error raised by the neural network-based optimizing model, which is due to the extension of focusing scope by the artificial immune mechanism. Meanwhile, the proposed model with the corresponding software can conduct optimization in two directions, namely, the process optimization and category development, and the corresponding results outperform those with an ordinary neural network-based intelligent model. It is also proved that the proposed model has the potential to act as a valuable tool from which the engineers and decision makers of the spinning process could benefit.

Traditional clustering techniques are inapplicable to problems where the relationships between data points evolve over time. Not only is it important for the clustering algorithm to adapt to the recent changes in the evolving data, but it also needs to take the historical relationship between the data points into consideration. In this paper, we propose ECKF, a general framework for evolutionary clustering large-scale data based on low-rank kernel matrix factorization. To the best of our knowledge, this is the first work that clusters large evolutionary data sets by the amalgamation of low-rank matrix approximation methods and matrix factorization-based clustering. Since the low-rank approximation provides a compact representation of the original matrix, and especially, the near-optimal low-rank approximation can preserve the sparsity of the original data, ECKF gains computational efficiency and hence is applicable to large evolutionary data sets. Moreover, matrix factorization-based methods have been shown to effectively cluster high-dimensional data in text mining and multimedia data analysis. From a theoretical standpoint, we mathematically prove the convergence and correctness of ECKF, and provide detailed analysis of its computational efficiency (both time and space). Through extensive experiments performed on synthetic and real data sets, we show that ECKF outperforms the existing methods in evolutionary clustering.

The stretching process is one of the key sections in fiber production, which is decisive to the quality of the final fiber products. Such a process raises high requirements on the control of the rollers with proper stretching ratios, and the large number of rollers with their special characteristics and the demand for synchronous running usually make the design of a good control scheme difficult. In this paper, a novel bioinspired multilayered intelligent cooperative controller (BMLICC) is proposed to provide a control plan for the interlinked rollers by organizing them into unified stretching units. Based on the multilayer regulation networks of neuroendocrine system in the human body, a networked controller structure is established. It consists of several components like rollers, distributed controllers, communication paths, and conversion units. The rollers in the same unit can exchange the working information rapidly to implement simultaneous response and cooperation. The stretching ratio can be kept stable and has strong resistance against the external disturbances on the stretching system. Both computer-simulation- and device-based experimental results demonstrate that the stretching unit with the proposed BMLICC can maintain its stretching ratio and effectively resist the external disturbances. This is beneficial to improve the performance of the stretched precursors and, furthermore, produce fibers with high quality. The proposed BMLICC can be easily extended to productions with multiple stretching units or industrial processes with similar mechanical structures for better control quality.

Bio-networks have the natural advantages of autonomy, scalability, and adaptability which are challenges for computer networks, especially P2P networks. We present a bio-inspired dynamic balanced tree structure network (called bio-block) based dynamic network. Every bio-block is a unique bio-entities collection with emergent service. This network has two parts, non-Service part (bio-entity is unit node) and in-Service part (bio-block is unit node). Useful bio-entities are dynamically transferring between these two part to keep the balance, and improve resources usage. This network inherits the balanced structure and O(nlogN) search steps with total N resources and n resources service request. It also eliminates redundancies by taking advantage of strong adaptability of bio-network which are composed of bio-entities. Any node in this balanced tree structured network can join and leave dynamically. Intensive experimental results show that the state of this network is converged when service distribution is stable. Moreover, theoretical results support an efficient search operation.

Network services in the future Internet are required to be highly available, ubiquitous, self-managing, and adaptable to dynamic environments. A promising way to implement such network services is combining mobile agent approaches with ecosystem-inspired evolutionary approaches. However, this perspective is still far from being practically implemented. An important reason is a lack of fundamental theory on macrodynamics of mobile agents in the new paradigm. This paper considers a type of large-scale mobile agent system and discusses the macrodynamics mainly affected by mobile agents' migration behaviors in the systems. The study benefits not only the design of composite services emerged from mobile agents in a type of ecosystem-inspired network architecture but also the future deployment of an Internet-scale system that holds myriads of mobile agents, hosts, and migratory behaviors of mobile agents.

Abstract:Corporate financial crisis forecasting plays an increasingly important role in the current intense and competitive commercial environment. It is an important phase in financial crisis forecasting to find the features that discriminate different financial conditions. In this paper, a genetic algorithm (GA) based approach and statistical filter approaches are applied to identify the best features for the support vector machine (SVM). The proposed GA‐based approach is carefully designed in order to have the capability of simultaneously optimizing the features and parameters of the SVM. Experimental results on the data from Chinese companies show that the GA‐based approach can extract fewer features with a higher accuracy compared with statistical filter approaches, such as analysis of variance, the T‐W test (which is the t test applied to variables satisfying a normal distribution and the Wilcoxon test applied to other variables not satisfying a normal distribution), logit regression and multiple discriminant analysis. Moreover, the experiments indicate that the proposed GA‐based approach is robust and suitable for selecting features for the SVM.

The topology control is a very important issue in wireless sensor networks (WSNs). Many approaches have been proposed to carry out in this aspect, including modern heuristic approach. In this paper, the Topology Control based on Artificial Immune Algorithm (ToCAIA) is proposed to solute the energy-aware topology control for WSNs. ToCAIA is a heuristic algorithm, which is heuristic from the immune system of human. In ToCAIA, the antibody is the solution of the problem, and the antigen is the problem. ToCAIA could be used to solve the multi-objective minimum energy network connectivity (MENC) problem, and get the approximate solution. The experiment result shows that the topology control by using ToCAIA can be utilized for WSNs network optimization purposes.

This paper introduces a new monocular stereo-vision sensor system which is suitable for visual servo control of parallel manipulator, consisting of one CCD camera and four pairs of symmetry mirrors, is designed for close quarters visual detection and motion control. The trinocular matching models is established from the geometric parameters, such as the angle between two face to face symmetric mirrors, angle and distance between two pairs of mirrors and the distance between CCD camera and mirrors. The trinocular Longuet-Higgins criteria has been introduced. The sensor performance and measuring accuracy are analyzed, which is necessary for the optimal design of the sensor. The binocular and trinocular search areas based on epipolar line theory are compared, it is proved that the search area is obviously decreased by using trinocular Longuet-Higgins criterion.

This paper presents an intelligent technology based method for analyzing and interpreting sensory data provided by multiple panels in evaluation of industrial products. In order to process the uncertainty existing in these sensory data, we first transform all sensory data on an unified optimal scale. Based on these normalized data sets, we compute the dissimilarities or distances between different panels and between different evaluation terms used by them, defined according to the degree of consistency of data variation. The obtained distances are then transformed into fuzzy numbers for physical interpretation. These fuzzy distances permit to characterize the evaluation behaviour of each panel and the quality of the evaluation terms used. Also, based on a Genetic Algorithm with punishment policy and the dissimilarity between terms, we develop a procedure for interpreting terms of one panel using those of another panel. This method has been applied to the fabric hand evaluation for a number of samples of knitted cotton in order to identify consumers' preference of different populations.

In this paper, we introduce an emergence modeling approach to the study of e-service composition, which is inspired by the characteristics of emergence and self-evolution in biological neuroendocrine and immune system. E-services are represented by autonomous bio-entities (mobile agents with biological operation), each bio-entity is described by a Melay state machine. The request of integrating complex processes is translated to an automata analysis problem. Bio-entities establish emergent network based on the matching message to provide e-service composition. Affinity is a parameter which can measure the message matching ability of the bio-entity integrally, it depends on three factors, namely, the matching strength of message, the e-service quality score, and the trust. In this way, the method completes a series of work from composition to management autonomously. The simulation results show that the approach can significantly improve the e-service composition performance. It adapts well to the changes of dynamic environments

Grid technologies facilitate innovative applications among dynamic virtual organizations, while the ability to deploy, manage, and properly remain functioning via traditional approaches has been exceeded by the complexity of the next generation of grid systems. An important method for addressing this challenge may require nature-inspired computing paradigms. This technique will entail construction of a bottom-up multiagent system; however, the appropriate implementation mechanism is under consideration in order for the autonomous and distributed agents to emerge as a controlled grid service or application. A credit card management service in economic interactions is considered in this article for a decentralized control approach. This consideration is based on a preliminarily developed ecological network-based grid middleware that has features desired for the next generation grid systems. The control scheme, design, and implementation of the credit card management service are presented in detail. The simulation results show that (1) agents are accountable for their activities such as behavior invocation, service provision, and resource utilization and (2) generated services or applications adapt well to dynamically changing environments such as agent amounts as well as partial failure of agents. The approach presented herein is beneficial for building autonomous and adaptive grid applications and services. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 1269–1288, 2006.

Spike (S) protein of severe acute respiratory syndrome coronavirus (SARS-CoV) associates with cellular receptors to mediate infection of their target cells. This paper studies the genome sequences of S protein and SARS 's receptor based on Z curve method. A visualization characteristic for SARS-CoV has been seized. The comparison results among a large number of genome sequences have proved such characteristic is special for SARS-CoV. Such characteristic can be looked upon as a fingerprint map of SARS-CoV to diagnose a suspected case. Also, this conclusion has been proved in recent real case in Guang Dong, China. Via our study in this paper, we have viewed that: (1) the fingerprint map can provide a nonstandard, simple and visualized detection approach, especially for the situation where the standard method's results are conflict, and (2) we can take virus and its receptor into coordinated consideration of sequence analysis for SARS-CoV detection.

Next-generation grid systems where the emphasis shifts to distributed global collaboration, a service-oriented approach, and information layer issues exhibit a strong sense of automation. Requirements for these systems resemble the self-organizing and the self-healing properties of natural ecosystems. Inspired by this resemblance, we introduce some key ecological concepts and mechanisms into the design for the third-generation grid systems. In this article, a novel Ecological Network-based Grid Middleware (ENGM), which is based on Ecological Network Computing Environment (ENCE), is proposed. First, we discuss how to design the ENCE by agent-oriented approaches based on the key concepts and principles of ecosystems. ENCE provides a new computing and problem-solving paradigm by combining natural ecosystem mechanisms with agent technologies. Then, we design the ENGM with built-in mechanisms to support desirable requirements of new grid systems, namely scalability, adaptability, self-organization, simplicity, and survivability. Based on Jeffery's conceptual model, we also present a corresponding grid-computing prototype that embeds ENGM layers from the implementation point of view. The ENGM will be useful to address the challenges of the third-generation grid systems. Finally, as a demonstration, we built an ENGM platform-based commercial grid service environment and developed a prototype of enterprise supply chain management system. The experimental results demonstrate that the proposed ENGM satisfies the requirements of the next-generation grid and is suitable for new generation grid applications. © 2004 Wiley Periodicals, Inc. Int J Int Syst 19: 859–884, 2004.

A new bio-network model for network emergence computation based on mutual-coupled immune network hypothesis in biological immune systems is proposed. The model is useful for service emergence, which is implemented by the super-entities generated by the interaction among the bio-entities created in the bio-network architecture. The bio-entities, the super-entities, and the interaction among the super-entities are designed and implements in our bio-network simulation platform. The emergent services generated from the mutual-coupled immune network computation model can satisfy various users' requirements. Finally, The feasibility of the model is demonstrated for the supply chain formation and reformation through computer simulation experiments on the platform. The simulation results demonstrate that the immune network model developed in our platform can be employed to complicated enterprise applications.

Next generation grid systems where the emphasis shifts to distributed global collaboration, a service-oriented approach and information layer issues exhibit a strong sense of automation. Requirements for these systems resemble the self-organizing and the healing properties of natural ecological networks. Inspired by this resemblance, we introduce some key ecological concepts and mechanisms into the design for the next generation grid systems. In this paper, a novel Ecological Network-based Grid Middleware (ENGM), which is based on Ecological Network Computing Environment (ENCE), is proposed. This middleware system can address the challenges of the next generation grid systems. That is, it achieves built-in mechanisms to support desirable requirements of new grid systems such as self-organization, survivability, self-evolution, scalability, adaptability, and dynamic configuration and reconfiguration.

In this article, we propose a new approach to the virus DNA–based evolutionary algorithm (VDNA-EA) to implement self-learning of a class of Takagi-Sugeno (T-S) fuzzy controllers. The fuzzy controllers use T-S fuzzy rules with linear consequent, the generalized input fuzzy sets, Zadeh fuzzy logic and operators, and the generalized defuzzifier. The fuzzy controllers are proved to be nonlinear proportional-integral (PI) controllers with variable gains. The fuzzy rules are discovered automatically and the design parameters in the input fuzzy sets and the linear rule consequent are optimized simultaneously by the VDNA-EA. The VDNA-EA uses the VDNA encoding method that stemmed from the structure of the VDNA to encode the design parameters of the fuzzy controllers. We use the frameshift decoding method of the VDNA to decode the DNA chromosome into the design parameters of the fuzzy controllers. In addition, the gene transfer operation and bacterial mutation operation inspired by a microbial evolution phenomenon are introduced into the VDNA-EA. Moreover, frameshift mutation operations based on the DNA genetic operations are used in the VDNA-EA to add and delete adaptively fuzzy rules. Our encoding method can significantly shorten the code length of the DNA chromosomes and improve the encoding efficiency. The length of the chromosome is variable and it is easy to insert and delete parts of the chromosome. It is suitable for complex knowledge representation and is easy for the genetic operations at gene level to be introduced into the VDNA-EA. We show how to implement the new method to self-learn a T-S fuzzy controller in the control of a nonlinear system. The fuzzy controller can be constructed automatically by the VDNA-EA. Computer simulation results indicate that the new method is effective and the designed fuzzy controller is satisfactory. © 2003 Wiley Periodicals, Inc.

A new DNA-based evolutionary algorithm (DNA-EA) for automatic design of a class of Takagi-Sugeno (TS) fuzzy controllers is discussed in this paper. The fuzzy rules are automatically discovered, and the design parameters in the input fuzzy sets and the linear rule consequent are optimized simultaneously by the DNA-EA. The DNA-EA uses the DNA encoding method stemmed from the structure of the biological DNA to encode the design parameters of the fuzzy controllers. The gene transfer operation and bacterial mutation operation inspired by a microbial evolution phenomenon are introduced into the DNA-EA. Moreover, frameshift mutation operations based on the DNA genetic operations are used in the DNA-EA. Our encoding method is suitable for complex knowledge representation, and is easy for the genetic operations at gene level to be introduced into the DNA-EA. The length of the chromosome is variable and it is easy to insert and delete parts of the chromosome. As a demonstration, we show how to implement the new method to design automatically a TS fuzzy controller in the control of a nonlinear system. The fuzzy controller can be automatically constructed by the DNA-EA. Computer simulation results indicate that the new method is effective and the designed fuzzy controller is satisfactory.

A new DNA-based immune genetic algorithm (DNA-IGA) is proposed to design a class of Takagi-Sugeno fuzzy control systems. The DNA-IGA is developed from the structures of biological immune system and DNA. The genetic operators of the method are based on the DNA genetic operations and evaluation of immune system. It uses DNA encoding method stemmed from the biological DNA to encode the design parameters of the fuzzy controllers. The DNA encoding method can significantly shorten the code length of antibodies and is suitable for complex knowledge representation. The proposed method has the local search ability and prevents the premature convergence. As a demonstration, we show how to implement the new method to optimize the design parameters of the TS fuzzy controller in control of a nonlinear system. Computer simulation indicates that the designed fuzzy controller is satisfactory.

In this paper, we focus on design of the efficient algorithms and tools for search and retrieval of documents on Internet. We propose an Internet information retrieval model based on mobile agent technique, fuzzy index algorithm, and genetic search methods. The whole work frame and the structure of the model are discussed. Mobile agents can be used to optimize network traffic for distributed applications. Efficient search of documents can be done with genetic algorithm that exploits the principles of temporal and spatial locality. Fuzzy index algorithm is used to intelligently evaluate the importance of the information searched on Internet. We have also implemented a software platform for experimenting with intelligent information retrieval on Internet using Java language. The experimental results demonstrate the effectiveness of our model.

A new universal controller structure suitable for various low or high order plants is presented. The controller includes a basic fuzzy self-tuning P-type immune controller inspired from the immune feedback mechanism of biological immune systems and an incremental block with the purpose to treat with the order of the plants. The P-type immune feedback law is automatically tuned by a fuzzy logic controller. The effectiveness of this control approach is examined by designing a fuzzy self-tuning PID-type immune controller to control tissue temperature during hyperthermia. Computer simulation results demonstrate that the control performance of the immune controller is superior to that of the corresponding linear controller.

Laser thermal therapies have shown great promise for minimally invasive treatment of benign and malignant tissue lesions. The paper presents a time-varying fuzzy control laser system to control tissue temperature in fresh pig liver samples for coagulation at 65/spl deg/C and tissue welding at 85/spl deg/C. The technical difficulties of our study lie in two facts: (1) laser tissue heating is a highly nonlinear time-varying dynamic process, and (2) the output power of the 810 nm diode laser that we used is not adjustable during operation. We have overcome the difficulties by regulating the laser via a variable on-off signal sequence, which is computed, at each sampling time, by our nonlinear time-varying fuzzy controller. The structure of the fuzzy controller is analytically analyzed and the result is related to the PD controller. Our experiments using the fuzzy control system show that the average of the absolute values of the maximum temperature derivations for three coagulation experiments and three welding experiments is 0.45/spl deg/C and 2.90/spl deg/C, respectively.

A new DNA genetic algorithm (DNA-GA) based on the mechanism of biological DNA and genetic information is proposed. The genetic operators of the DNA-GA are discussed. The DNA encoding method is suitable for the representation of complex knowledge. The DNA-GA is employed to design effective generalized fuzzy systems (GFS) for the modeling and control applications. GFS employ arbitrary fuzzy rules, e(-|/spl alpha//sub 1/x/sub 1/+/spl alpha//sub 2/|)-type input fuzzy sets containing almost arbitrary continuous input fuzzy sets, arbitrary singleton output fuzzy sets, arbitrary fuzzy logic AND, and the generalized defuzzifier containing the widely-used centroid defuzzifier as a special case. The DNA-GA is used to select input variables and to tune the design parameters and membership functions of GFS. As such, the fuzzy rule sets of GFS can be obtained. The work in this paper provides a useful way for the design of fuzzy controllers and fuzzy models.

We propose a new DNA-based genetic algorithm (DNA-GA) to optimize the design parameters of a generalized membership-type Takagi-Sugeno fuzzy controller (GTSFC). The GTSFC employs TS fuzzy rules with linear consequent, e/sup -|ax+b|(c)/-type input fuzzy sets containing almost arbitrary continuous input fuzzy sets, Zadeh fuzzy logic AND operation, and the widely-used centroid defuzzier. The GTSFC is proved to be a nonlinear PI controller with variable gains. The optimized design parameters are the input fuzzy sets and the linear consequent of the rules. The DNA-GA uses a DNA encoding method stemmed from the structure of the biological DNA to encode the design parameters of the GTSFC. The genetic operators of the method are based on the DNA genetic operations. The encoding method can significantly shorten the code length of DNA chromosomes and is suitable for complex knowledge representation. As a demonstration, we show how to implement the new method to optimize the design parameters of the GTSFC to control a nonlinear system. Computer simulation results indicate that the performance of the designed fuzzy controller is satisfactory.

Recent studies have shown that both Mamdani-type and Takagi-Sugeno-type fuzzy systems are universal approximators in that they can uniformly approximate continuous functions defined on compact domains with arbitrarily high approximation accuracy. In this paper, we investigate necessary conditions for general multiple-input single-output (MISO) Mamdani fuzzy systems as universal approximators with as minimal system configuration as possible. The general MISO fuzzy systems employ almost arbitrary continuous input fuzzy sets, arbitrary singleton output fuzzy sets, arbitrary fuzzy rules, product fuzzy logic AND, and the generalized defuzzifier containing the popular centroid defuzzifier as a special case. Our necessary conditions are developed under the practically sensible assumption that only a finite set of extrema of the multivariate continuous function to be approximated is available. We have first revealed a decomposition property of the general fuzzy systems: A r-input fuzzy system can always be decomposed to the sum of r simpler fuzzy systems where the first system has only one input variable, the second one two input variables, and the last one r input variables. Utilizing this property, we have derived some necessary conditions for the fuzzy systems to be universal approximators with minimal system configuration. The conditions expose the strength as well as limitation of the fuzzy approximation: (1) only a small number of fuzzy rules may be needed to uniformly approximate multivariate continuous functions that have a complicated formulation but a relatively small number of extrema; and (2) the number of fuzzy rules must be large in order to approximate highly oscillatory continuous functions. A numerical example is given to demonstrate our new results.

Both Takagi-Sugeno (TS) and Mamdani fuzzy systems are known to be universal approximators. We investigate whether one type of fuzzy approximators is more economical than the other. The TS fuzzy systems are the typical two-input single-output TS fuzzy systems. We first establish necessary conditions on minimal system configuration of the TS fuzzy systems as function approximators. We show that the number of the input fuzzy sets and fuzzy rules needed by the TS fuzzy systems depend on the number and locations of the extrema of the function to be approximated. The resulting conditions reveal the strength of the TS fuzzy approximators. The drawback, though, is that a large number of fuzzy rules must be employed to approximate periodic or highly oscillatory functions. We then compare these necessary conditions with the ones that we established for the general Mamdani fuzzy systems in our previous papers. Results of the comparison unveil that the minimal system configurations of the TS and Mamdani fuzzy systems are comparable. Finally, we prove that the minimal configuration of the TS fuzzy systems can be reduced and becomes smaller than that of the Mamdani fuzzy systems if nontrapezoidal or nontriangular input fuzzy sets are used. We believe that all the results in present paper hold for the TS fuzzy systems with more than two input variables but the proof seems to be mathematically difficult. Our new findings are valuable in designing more compact fuzzy systems, especially fuzzy controllers and models which are two most popular and successful applications of the fuzzy approximators.