Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
CMAC with general basis functions
Neural Networks
NLq theory: checking and imposing stability of recurrentneural networks for nonlinear modeling
IEEE Transactions on Signal Processing
Optimal design of CMAC neural-network controller for robotmanipulators
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Analysis and design of hierarchical fuzzy systems
IEEE Transactions on Fuzzy Systems
Hybrid adaptive fuzzy identification and control of nonlinear systems
IEEE Transactions on Fuzzy Systems
Approximation Capabilities of Hierarchical Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Input-to-state stability for discrete-time nonlinear systems
Automatica (Journal of IFAC)
Stable dynamic backpropagation learning in recurrent neural networks
IEEE Transactions on Neural Networks
A self-organizing HCMAC neural-network classifier
IEEE Transactions on Neural Networks
Learning and convergence analysis of neural-type structured networks
IEEE Transactions on Neural Networks
FCMAC-Yager: A Novel Yager-Inference-Scheme-Based Fuzzy CMAC
IEEE Transactions on Neural Networks
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In the control theory area one of the most successful schemes is the indirect adaptive control strategy; however, one of the most important problems of this approach is the identification phase, because only if an excellent approximation of the nonlinear system is achieved the controller will show a good performance. In this paper we present a novel approach of an indirect adaptive control using hierarchical fuzzy CMAC neuronal networks. We show the full design and the stability analysis of this new structure. Experiment results are obtained via our prototype of the ball and plate system.