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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Chen, G. H., Lin, C. J., & Lee, C. Y. (2007). Efficient reinforcement hybrid evolutionary learning for recurrent wavelet-based neuro-fuzzy systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4570 LNAI, pp. 207–216). Springer Verlag. https://doi.org/10.1007/978-3-540-73325-6_21
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