Intelligent adaptive control for MIMO uncertain nonlinear systems
Expert Systems with Applications: An International Journal
A Novel Associative Memory System Based Modeling and Prediction of TCP Network Traffic
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
A novel associative memory approach to speech enhancement in a vehicular environment
Expert Systems with Applications: An International Journal
CMAC-based compensator for limiting bound required in supervisory control systems
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Standalone CMAC control system with online learning ability
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
CMAC neural networks structures
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
Adaptive filter design using recurrent cerebellar model articulation controller
IEEE Transactions on Neural Networks
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The cerebellar model articulation controller (CMAC) is a simple and fast neural-network based on local approximations. However, its rigid structure reduces its accuracy of approximation and speed of convergence with heterogeneous inputs. In this paper, we propose a generalized CMAC (GCMAC) network that considers different degrees of generalization for each input. Its representation abilities are analyzed, and a set of local relationships that the output function must satisfy are derived. An adaptive growing method of the network is also presented. The validity of our approach and methods are shown by some simulated examples