Frontiers of Electrical and Electronic Engineering >
Optimization of fuzzy CMAC using evolutionary Bayesian Ying-Yang learning
Received date: 07 Jul 2010
Accepted date: 28 Feb 2011
Published date: 05 Jun 2011
Copyright
Cerebellar model articulation controller (CMAC) is a popular associative memory neural network that imitates human’s cerebellum, which allows it to learn fast and carry out local generalization efficiently. This research aims to integrate evolutionary computation into fuzzy CMAC Bayesian Ying-Yang (FCMAC-BYY) learning, which is referred to as FCMAC-EBYY, to achieve a synergetic development in the search for optimal fuzzy sets and connection weights. Traditional evolutionary approaches are limited to small populations of short binary string length and as such are not suitable for neural network training, which involves a large searching space due to complex connections as well as real values. The methodology employed by FCMAC-EBYY is coevolution, in which a complex solution is decomposed into some pieces to be optimized in different populations/species and then assembled. The developed FCMAC-EBYY is compared with various neuro-fuzzy systems using a real application of traffic flow prediction.
Payam S. RAHMDEL , Minh Nhut NGUYEN , Liying ZHENG . Optimization of fuzzy CMAC using evolutionary Bayesian Ying-Yang learning[J]. Frontiers of Electrical and Electronic Engineering, 2011 , 6(2) : 208 -214 . DOI: 10.1007/s11460-011-0145-z
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