標題: | Reinforcement evolutionary learning using data mining algorithm with TSK-type fuzzy controllers |
作者: | Hsu, Chi-Yao Hsu, Yung-Chi Lin, Sheng-Fuu 電機工程學系 Department of Electrical and Computer Engineering |
關鍵字: | Fuzzy system;Control;Symbiotic evolution;Reinforcement learning;Association rules;R-ELDMA |
公開日期: | 1-四月-2011 |
摘要: | Reinforcement evolutionary learning using data mining algorithm (R-ELDMA) with a TSK-type fuzzy controller (TFC) for solving reinforcement control problems is proposed in this study. R-ELDMA aims to determine suitable rules in a TFC and identify suitable and unsuitable groups for chromosome selection. To this end, the proposed R-ELDMA entails both structure and parameter learning. In structure learning, the proposed R-ELDMA adopts our previous research - the self-adaptive method (SAM) - to determine the suitability of TFC models with different fuzzy rules. In parameter learning, the data-mining based selection strategy (DSS), which proposes association rules, is used. More specifically, DSS not only determines suitable groups for chromosomes selection but also identifies unsuitable groups to be avoided selecting chromosomes to construct a TFC. Illustrative examples are presented to show the performance and applicability of the proposed R-ELDMA. Crown Copyright (C) 2010 Published by Elsevier B. V. All rights reserved. |
URI: | http://dx.doi.org/10.1016/j.asoc.2010.12.027 http://hdl.handle.net/11536/9113 |
ISSN: | 1568-4946 |
DOI: | 10.1016/j.asoc.2010.12.027 |
期刊: | APPLIED SOFT COMPUTING |
Volume: | 11 |
Issue: | 3 |
起始頁: | 3247 |
結束頁: | 3259 |
顯示於類別: | 期刊論文 |