Optimization of fuzzy CMAC using evolutionary Bayesian Ying-Yang learning

doi:10.1007/s11460-011-0145-z

Front Elect Electr Eng Chin

2011, Vol. 6

Issue (2) : 208-214 https://doi.org/10.1007/s11460-011-0145-z

RESEARCH ARTICLE

Optimization of fuzzy CMAC using evolutionary Bayesian Ying-Yang learning

Payam S. RAHMDEL¹(

), Minh Nhut NGUYEN², Liying ZHENG³

1. School of Engineering and Information Sciences, Middlesex University, London NW4 4BT, United Kingdom; 2. Institute for Infocomm Research, Singapore 138632, Singapore; 3. School of Computer Science and Techology, Harbin Engineering University, Harbin 150001, China

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Abstract

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.

Keywords cerebellar model articulation controller (CMAC) Bayesian Ying-Yang (BYY) learning evolutionary computation

Corresponding Author(s): RAHMDEL Payam S.,Email:P.rahmdel@mdx.ac.uk

Issue Date: 05 June 2011

Cite this article:

Payam S. RAHMDEL,Minh Nhut NGUYEN,Liying ZHENG. Optimization of fuzzy CMAC using evolutionary Bayesian Ying-Yang learning[J]. Front Elect Electr Eng Chin, 2011, 6(2): 208-214.

URL:

https://academic.hep.com.cn/fee/EN/10.1007/s11460-011-0145-z
https://academic.hep.com.cn/fee/EN/Y2011/V6/I2/208

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