A function estimation approach to sequential learning with neural networks
Neural Computation
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Fuzzy engineering
Self-Organizing Gaussian Fuzzy CMAC with Truth Value Restriction
ICITA '05 Proceedings of the Third International Conference on Information Technology and Applications (ICITA'05) Volume 2 - Volume 02
Smooth trajectory tracking of three-link robot: a self-organizingCMAC approach
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Dynamic fuzzy neural networks-a novel approach to functionapproximation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A GA-based method for constructing fuzzy systems directly from numerical data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An online self-constructing neural fuzzy inference network and its applications
IEEE Transactions on Fuzzy Systems
Self-constructing fuzzy neural network speed controller for permanent-magnet synchronous motor drive
IEEE Transactions on Fuzzy Systems
Self-organizing neuro-fuzzy system for control of unknown plants
IEEE Transactions on Fuzzy Systems
Pseudoerror-based self-organizing neuro-fuzzy system
IEEE Transactions on Fuzzy Systems
Combination of online clustering and Q-value based GA for reinforcement fuzzy system design
IEEE Transactions on Fuzzy Systems
Orthogonal least squares learning algorithm for radial basis function networks
IEEE Transactions on Neural Networks
Fuzzy basis functions, universal approximation, and orthogonal least-squares learning
IEEE Transactions on Neural Networks
Using recurrent neural networks for adaptive communication channel equalization
IEEE Transactions on Neural Networks
Adaptive PI Hermite neural control for MIMO uncertain nonlinear systems
Applied Soft Computing
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A cerebellar model arithmetic computer (CMAC)-based neuron-fuzzy approach for accurate system modeling is proposed. The system design comprises the structure determination and the hybrid parameter learning. In the structure determination, the CMAC-based system constitution is used for structure initialization. With the advantage of generalization of CMAC, the initial receptive field constitution is formed in a systematic way. In the parameter learning, the random optimization algorithm (RO) is combined with the least square estimation (LSE) to train the parameters, where the premises and the consequences are updated by RO and LSE, respectively. With the hybrid learning algorithm, a compact and well-parameterized CMAC can be achieved for the required performance. The proposed work features the following salient properties: (1) good generalization for system initialization; (2) derivative-free parameter update; and (3) fast convergence. To demonstrate potentials of the proposed approach, examples of SISO nonlinear approximation, MISO time series identification/prediction, and MIMO system mapping are conducted. Through the illustrations and numerical comparisons, the excellences of the proposed work can be observed.