Title: | Reinforcement hybrid evolutionary learning for recurrent wavelet-based neurofuzzy systems |
Authors: | Lin, Cheng-Jian Hsu, Yung-Chi 電控工程研究所 Institute of Electrical and Control Engineering |
Keywords: | control;genetic algorithms;neurofuzzy system;recurrent network;reinforcement learning |
Issue Date: | 1-Aug-2007 |
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 R-HELA 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. Illustrative examples were conducted to show the performance and applicability of the proposed R-HELA method. |
URI: | http://dx.doi.org/10.1109/TFUZZ.2006.889920 http://hdl.handle.net/11536/10506 |
ISSN: | 1063-6706 |
DOI: | 10.1109/TFUZZ.2006.889920 |
Journal: | IEEE TRANSACTIONS ON FUZZY SYSTEMS |
Volume: | 15 |
Issue: | 4 |
Begin Page: | 729 |
End Page: | 745 |
Appears in Collections: | Articles |
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