標題: Genetic reinforcement learning through symbiotic evolution for fuzzy controller design
作者: Juang, CF
Lin, CT
電控工程研究所
Institute of Electrical and Control Engineering
關鍵字: genetic algorithm;symbiotic evolution;fuzzy controller
公開日期: 1998
摘要: An efficient genetic reinforcement learning algorithm for designing fuzzy controllers is proposed in this paper. (1) The genetic algorithm (GA) adopted in this paper is based upon symbiotic evolution which, when applied to fuzzy controller design, matches well with the local mapping property of a fuzzy rule. Using this Symbiotic-Evolution-based Fuzzy Controller (SEFC) design method, the number of control trials as well as consumed CPU time are reduced considerably as compared to traditional GA-based fuzzy controller design methods and other types of genetic reinforcement learning schemes. The proposed SEFC design method has been applied to the cart-pole balancing system. Efficiency and superiority of the proposed SEFC have been verified from this problem and from the comparisons with traditional GA-based fuzzy systems.
URI: http://hdl.handle.net/11536/19585
ISBN: 0-7803-4863-X
期刊: 1998 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AT THE IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE - PROCEEDINGS, VOL 1-2
起始頁: 1281
結束頁: 1285
顯示於類別:會議論文