標題: Reinforcement hybrid evolutionary learning for recurrent wavelet-based neurofuzzy systems
作者: Lin, Cheng-Jian
Hsu, Yung-Chi
電控工程研究所
Institute of Electrical and Control Engineering
關鍵字: control;genetic algorithms;neurofuzzy system;recurrent network;reinforcement learning
公開日期: 1-八月-2007
摘要: 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
期刊: IEEE TRANSACTIONS ON FUZZY SYSTEMS
Volume: 15
Issue: 4
起始頁: 729
結束頁: 745
顯示於類別:期刊論文


文件中的檔案:

  1. 000248703700016.pdf

若為 zip 檔案,請下載檔案解壓縮後,用瀏覽器開啟資料夾中的 index.html 瀏覽全文。