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Erschienen in:
2007 | OriginalPaper | Buchkapitel
Efficient Reinforcement Hybrid Evolutionary Learning for Recurrent Wavelet-Based Neuro-fuzzy Systems
verfasst von : Cheng-Hung Chen, Cheng-Jian Lin, Chi-Yung Lee
Erschienen in: New Trends in Applied Artificial Intelligence
Verlag: Springer Berlin Heidelberg
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This paper proposes a recurrent wavelet-based neuro-fuzzy 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. An illustrative example was conducted to show the performance and applicability of the proposed R-HELA method.