The improved CMAC model and learning result analysis

Authors:
Daqi Zhu;Min Kong;YonQing Yang
Affiliations:
Research Centre of Control Science and Engineering, Southern Yangtze University, Wu Xi, Jiangshu Province, China;Research Centre of Control Science and Engineering, Southern Yangtze University, Wu Xi, Jiangshu Province, China;Department of mathematics, Southern Yangtze University, Wu Xi, Jiangshu Province, China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Year:
2005

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Abstract

An improved neural networks online learning scheme is proposed to speed up the learning process in cerebellar model articulation controllers(CMAC). The improved learning approach is to use the learned times of the addressed hypercubes as the credibility (confidence) of the learned values in the early learning stage, and the updating data for addressed hypercubes is proportional to the inverse of the exponent of learned times, in the later stage the updating data for addressed hypercubes is proportional to the inverse of learned times. With this idea, the learning speed can indeed be improved.