A Modified CMAC Algorithm Based on Credit Assignment
Neural Processing Letters
Cybernetics and Systems Analysis
Closed-loop method to improve image PSNR in pyramidal CMAC networks
International Journal of Computer Applications in Technology
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
Adaptive Growing Quantization for 1D CMAC Network
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Kernel CMAC with Reduced Memory Complexity
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
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
CMAC neural networks structures
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
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An adaptive control algorithm based on Albus' CMAC (Cerebellar Model Articulation Controller) was studied with emphasis on how to train CMAC systems. Two training techniques-neighborhood sequential training and random training, have been devised. These techniques were used to generate mathematical functions, and both methods successfully circumvented the training interference resulting from CMAC's inherent generalization property. In the neighborhood sequential training method, a strategy was devised to utilize the discrete, finite state nature of the CMAC's address space for selecting points in the input space which would train CMAC systems in the most rapid manner possible. The random training method was found to converge on the training function with the greatest precision, although it requires longer training periods than the neighborhood sequential training method to achieve a desired performance level