Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neural Systems for Control
Foundations of Neuro-Fuzzy Systems
Foundations of Neuro-Fuzzy Systems
Observer-based adaptive fuzzy-neural control for unknown nonlineardynamical systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Identification and control of dynamic systems using recurrent fuzzy neural networks
IEEE Transactions on Fuzzy Systems
Self-constructing fuzzy neural network speed controller for permanent-magnet synchronous motor drive
IEEE Transactions on Fuzzy Systems
A neuro-fuzzy system modeling with self-constructing rule generationand hybrid SVD-based learning
IEEE Transactions on Fuzzy Systems
Online adaptive fuzzy neural identification and control of a class of MIMO nonlinear systems
IEEE Transactions on Fuzzy Systems
Supervisory recurrent fuzzy neural network control of wing rock for slender delta wings
IEEE Transactions on Fuzzy Systems
Pseudoerror-based self-organizing neuro-fuzzy system
IEEE Transactions on Fuzzy Systems
Hybrid supervisory control using recurrent fuzzy neural network for tracking periodic inputs
IEEE Transactions on Neural Networks
Neural-network predictive control for nonlinear dynamic systems with time-delay
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation
IEEE Transactions on Neural Networks
Self-Organizing Adaptive Fuzzy Neural Control for a Class of Nonlinear Systems
IEEE Transactions on Neural Networks
Indirect adaptive self-organizing RBF neural controller design with a dynamical training approach
Expert Systems with Applications: An International Journal
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In this paper, a dynamic recurrent fuzzy neural network (DRFNN) with a structure learning scheme is proposed. The structure learning scheme consists of two learning phases: the node-constructing phase and the node-pruning phase, which enables the DRFNN to determine the nodes dynamically to achieve optimal network structure. Then, a self-structuring recurrent fuzzy neural network control (SRFNNC) system via the DRFNN approach is developed. The SRFNNC system is composed of a neural controller and a compensation controller. The neural controller using a DRFNN to mimic an ideal controller is the main controller, and the compensation controller is designed to compensate the difference between the neural controller and the ideal controller. In the SRFNNC system, all the parameters are evolved based on the Lyapunov function to ensure the system stability. Finally, to investigate the effectiveness of the proposed SRFNNC system, it is applied to control a second-order chaotic nonlinear system. A comparison between a fixed-structuring recurrent fuzzy neural network control and the proposed SRFNNC is made. Through the simulation results, the advantages of the proposed SRFNNC method can be observed.