Efficient reinforcement hybrid evolutionary learning for recurrent wavelet-based neuro-fuzzy systems

Authors:
Cheng-Hung Chen;Cheng-Jian Lin;Chi-Yung Lee
Affiliations:
Dept. of Electrical and Control Engineering, National Chiao-Tung University, Hsinchu, Taiwan, R.O.C.;Dept. of Computer Science and Information Engineering, Chaoyang University of Technology, Wufeng, Taichung County, Taiwan, R.O.C.;Dept. of Computer Science and Information Engineering, Nankai Institute of Technology, Nantou County, Taiwan, R.O.C.
Venue:
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Year:
2007

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Abstract

This paper proposes a recurrent wavelet-based neurofuzzy system (RWNFS) with the reinforcement hybrid evolutionary learning algorithm (R-HELA) for solving various control problems. The proposed RHELA combines the compact genetic algorithm (CGA) and the modified variable-length genetic algorithm (MVGA), performs the structure/ parameter learning for dynamically constructing the RWNFS. That is, both the number of rules and the adjustment of parameters in the RWNFS are designed concurrently by the R-HELA. In the R-HELA, individuals of the same length constitute the same group. There are multiple groups in a population. The evolution of a population consists of three major operations: group reproduction using the compact genetic algorithm, variable two-part crossover, and variable two-part mutation. An illustrative example was conducted to show the performance and applicability of the proposed R-HELA method.