ABSTRACT Decision Neural Networks significantly improve the performance of complex models and create more transparent and accountable decision‐making systems that can be trusted in critical applications. However, their performance strongly depends on the amount of data and the learning algorithm. This article describes the development of a simplified structure and training algorithm based on the Levenberg–Marquardt algorithm to enhance the decision neural network's training and assess the utility function's efficacy in multi‐objective issues. The suggested algorithm converges faster than traditional algorithms. Also, the designed scheme combines gradient descent with the Gauss‐Newton method, allowing it to escape shallow local minima more effectively than other similar techniques. Numerical examples demonstrate how well the suggested method estimates linear utility functions, even complicated and nonlinear ones. Additionally, the findings of applying the enhanced decision neural network to multi‐objective decision‐making issues show that this instructional technique produces responses with higher quality and faster convergence. By applying the designed scheme to a multi‐objective problem with seven primary answers, it is shown that accuracy is improved by more than 20%.

This study presents a novel Multi-Agent Reinforcement Learning (MURL) architecture for autonomous vehicle (AV) navigation in complex urban traffic environments. By integrating a Social Value Orientation (SVO) model into a model-free SARSA reinforcement learning framework, our approach effectively balances individual agents’ social preferences with safety and performance objectives. A logistic regression-based risk assessment module evaluates collision probabilities in real time by analyzing spatiotemporal dynamics such as distances and velocities. Additionally, a dynamic optimizer adapts the learning rate and exploration strategies of the SARSA algorithm to provide efficient convergence to optimal policies. Extensive simulation experiments demonstrate that the proposed method significantly enhances safety and efficiency, achieving a 55.6% reduction in collision risk and increasing average rewards per episode by 2.1 compared to traditional SARSA without SVO. Furthermore, the optimized policy reduces average episode length, indicating the framework’s effectiveness in providing robust decision-making and adaptability across various traffic scenarios.

Recently, type-3 (T3) fuzzy logic systems (FLSs) have been widely used in various problems, such as modeling, control systems, image processing, forecasting problems, optimization algorithms, and many others. Most studies of T3-FLS focus on its different applications. However, the basic theory, the applications in real-time and online problems, learning schemes, and the robustness against non-Gaussian noises have been rarely studied. In this article, the simplification of T3-FLSs is taken into account, and the new membership functions (MFs), learning schemes, and type reduction are introduced. The concept of singleton MFs in adaptive fuzzy inference systems (ANFIS) is extended to T3-FLSs, and T3-ANFIS is proposed. The type reduction is simplified, and a noniterative learning scheme is developed. The corresponding computations for adaptation laws are derived, and all rules parameters and MF parameters are adjusted. To enhance the robustness versus impulsive noises, a T3-FLS-based correntropy Kalman filter (CKF) is designed. In the suggested algorithm, the kernel-size is not a constant value, but it is online updated by a T3-FLS. Also, to further improve robustness against noisy data, nonsingleton fuzzification for the suggested MF is formulated. By several simulations using real data sets, the feasibility of the suggested T3-FLS is shown, and its superiority is verified by comparisons. Also, the better robustness of suggested T3-FLS-based CKF versus impulsive noises is shown by comparison with traditional KFs.

The objective of the present investigation is to introduce a novel adaptive fractional‐order proportional‐integral‐derivative controller, which is characterized by the online tuning of its parameters by utilizing five distinct multilayer perceptron neural networks employing the extended Kalman filter. Utilizing the backpropagation algorithm in training a multilayer perceptron neural network is deemed effective in identifying the structural system and estimating the plant. The controller is applied using the Jacobian derived from the online estimated model. The utilization of adaptive interval type‐2 fuzzy neural networks in conjunction with the extended Kalman filter tuning method and feedback error learning strategy results in enhanced stability and robustness of the controller in the face of estimation error, seismic disturbances, and unknown nonlinear functions. The study aims to validate the efficacy of the proposed controller by examining its performance on a 20‐story nonlinear building. The numerical results show that including a compensator enhances the performance of the adaptive fractional‐order proportional‐integral‐derivative controller. The results show that the proposed adaptive fractional‐order proportional‐integral‐derivative controller has a better performance than other controllers and that the interstory drift ratio criterion under the El Centro earthquake with a magnitude of 1.5 times experienced an improvement of up to 65% compared to other controllers, and this amount in the Kobe earthquake reached more than 58%. Other criteria have also experienced significant improvement using the proposed controller.

This study proposes a novel control scheme for simultaneously tracking and stabilizing nonholonomic wheeled mobile robots (NWMRs) subject to actuator and sensor faults, measurement errors, uncertain dynamics, and time-varying slippage/skid disturbances. To this end, a nonlinear model based on a type-3 (T3) fuzzy logic system (FLS) is developed for NWMR tracking and stabilization. Furthermore, a nonlinear model predictive controller (NMPC) is designed analytically without employing iterative computations, thus achieving fast performance. A new approach of type-3 nonsingleton fuzzification is introduced to handle measurement errors. Additionally, faults in the actuators and sensors are detected by a supervisory scheme and eliminated by a devised compensator. Finally, extensive simulations and experimental validations are conducted to further verify the effectiveness of the proposed scheme, along with a comparative analysis of several benchmarking methods.

This paper presents a novel adaptive neurochaotic fuzzy control system based on type‐2 fuzzy systems to reduce seismic responses in multistory structures with active tuned mass dampers under near‐field and far‐field earthquakes. In this proposed control system, the whole parameters of the plant are assumed to be completely unknown, the structural model is estimated using a multilayer perceptron neural network, and the system’s Jacobian is extracted. The online estimation model is used, and the controller parameters are adaptively trained using the extended Kalman filter and error back‐propagation method. Subsequently, the control force is applied to the active tuned mass damper, and the control objectives are met. The adaptive controller does not require initial settings, and a fractional‐order proportional‐integral‐derivative controller is added to maximize stability and robustness against seismic vibration. A simple adaptive controller optimized by a particle swarm is also presented as an innovation. Comparing the performance of the improved simple adaptive controller and adaptive neurochaotic fuzzy controller, the proposed controllers appear more efficient and accurate. However, the superiority of the novel adaptive neurochaotic fuzzy over the improved simple adaptive controller in reducing maximum displacement, acceleration, drift, and base shear while maintaining acceptable performance under parametric uncertainties and seismic conditions is substantial.

Cardiovascular diseases are the leading cause of mortality, and accurate segmentation of ventricular regions incardiac magnetic resonance images (MRIs) is crucial for diagnosing and treating these diseases. However, fully automated and accurate right ventricle (RV) segmentation remains challenging due to the irregular cavities with ambiguous boundaries and mutably crescentic structures with relatively small targets of the RV regions in MRIs. In this article, a triple-path segmentation model, called FMMsWC, is proposed by introducing two novel image feature encoding modules, i.e., the feature multiplexing (FM) and multiscale weighted convolution (MsWC) modules, for the RV segmentation in MRIs. Considerable validation and comparative experiments were conducted on two benchmark datasets, i.e., the MICCAI2017 Automated Cardiac Diagnosis Challenge (ACDC), and the Multi-Centre, Multi-Vendor & Multi-Disease Cardiac Image Segmentation Challenge (M&MS) datasets. The FMMsWC outperforms state-of-the-art approaches, and its performance can approach that of the manual segmentation results by clinical experts, facilitating accurate cardiac index measurement for the rapid assessment of cardiac function and aiding diagnosis and treatment of cardiovascular diseases, which has great potential for clinical applications.

The aim of this article is to analyze the problem of fault estimation for mode-dependent interval type-2 fuzzy systems with quantized output measurements. Different from the existing fault estimation methods requiring the observer matching condition, a new fault estimation technique is proposed, wherein a stochastically intermediate variable subject to the information on operating modes is considered, under which a robust observer is constructed to simultaneously estimate the state and faults. Based on the strategy of linear matrix inequality, sufficient conditions are established to ensure that the states of resulting systems are bounded in probability sense. By offering three illustrative examples, in which two of them are practical models, namely, tunnel diode circuit system and Rössler system, the availability and feasibility of the proposed design method are explained.

The main reason of the extensive usage of the fuzzy systems in many branches of science is their approximation ability. In this paper, an interval type-3 fuzzy system (IT3FS) is proposed. The uncertainty modeling capability of the proposed IT3FS is improved in contrast to type-1 and type-2 fuzzy systems (T1FS and T2FS). Because in the proposed IT3FS, the membership is defined as an interval type-2 fuzzy set, whereas in T1FS and T2FS, the membership is crisp value and type-1 fuzzy set, respectively. An online fractional-order learning algorithm is given to optimize the consequent parameters of the IT3FS. The stability of the learning algorithm is proved by utilizing the Lyapunov method. The validity of the proposed fuzzy system is illustrated by both simulation and the experimental studies. It is shown that the proposed fuzzy system and associated learning algorithm result in better approximation performance in comparison with the other well-known approaches.

In this paper, a novel H∞-based adaptive fuzzy control is presented for the synchronization of fractional-order chaotic systems. A self-evolving nonsingleton type-2 fuzzy neural network (SE-NST2FNN) is proposed for the estimation of the unknown functions in the dynamics of the system. The effects of the approximation error and the external disturbances are eliminated by designing an adaptive compensator, such that the H∞norm of the synchronization error is minimized and asymptotically stability is achieved. The consequent parameters of SE-NST2FNN are tuned based on the adaptation laws that are derived from Lyapunov stability analysis. The antecedent part and the rule database of SE-NST2FNN are optimized based on a clustering method and the modified invasive weed optimization algorithm, respectively. The effectiveness of proposed control scheme is verified by simulation examples.

Two-mode adaptive controllers have two phases of operation: a learning phase and an operation phase. This paper presents a two-mode indirect adaptive control approach for the synchronization of chaotic systems, using a hierarchical interval type-2 fuzzy neural network (HT2FNN). Its contribution to the existing literature is the adaptation laws derived for the parameters of the membership functions, based on Lyapunov stability analysis. Since, in hierarchical case, each T2FNN has only two inputs, the computing of the derivatives is much simpler than the case in classical interval type-2 FNN. Moreover, a novel approach is presented for the compensation of the approximation error. The tuning of the parameters of the membership functions (MF) and the use of an interval type-2 FNN ensures that the estimation error is very small so that it can be negligible. Furthermore, the number of MF required is seen to be less than that needed with type-1 fuzzy sets. The simulation results confirm the efficacy of the proposed scheme in the synchronization of a uncertain nonidentical chaotic systems.