Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Robust adaptive control
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
CMAC with general basis functions
Neural Networks
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Optimal design of CMAC neural-network controller for robotmanipulators
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Observer-based adaptive fuzzy-neural control for unknown nonlineardynamical systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A systematic neuro-fuzzy modeling framework with application tomaterial property prediction
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A recurrent fuzzy-neural model for dynamic system identification
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
IEEE Transactions on Fuzzy Systems
Input-to-state stability for discrete-time nonlinear systems
Automatica (Journal of IFAC)
Recurrent neuro-fuzzy networks for nonlinear process modeling
IEEE Transactions on Neural Networks
Some new results on system identification with dynamic neural networks
IEEE Transactions on Neural Networks
Gait Pattern Based on CMAC Neural Network for Robotic Applications
Neural Processing Letters
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Normal fuzzy CMAC neural network performs well for nonlinear systems identification because of its fast learning speed and local generalization capability for approximating nonlinear functions. However, it requires huge memory and the dimension increases exponentially with the number of inputs. It is difficult to model dynamic systems with static fuzzy CMACs. In this paper, we use two types of recurrent techniques for fuzzy CMAC to overcome the above problems. The new CMAC neural networks are named recurrent fuzzy CMAC (RFCMAC) which add feedback connections in the inner layers (local feedback) or the output layer (global feedback). The corresponding learning algorithms have time-varying learning rates, the stabilities of the neural identifications are proven.