SELF-LEARNING FUZZY CONTROL RULES USING GENETIC ALGORITHMS |
| |
摘 要: | This papcr presents a new genetic algorithms(GAs)-based method for self-learniag fuzzy control rules. An improved GA is used to learn to optimally select the fuzzy membership functions of the linguistic labels in the condition portion of each rule, and to automatically generate fuzzy control actions under each condition. The dynamics of the controlled system is unknown to the GA. The only information for evaluating performance is a failure signal indicating that the controlled system is out of control. We compare its performance with that of other learning methods for the same problem. We also examine the ability of the algorithm to adapt to changing conditions. Simulation results show that such an approach for self-learning fuzzy control rules is both effective and robust.
|
SELF-LEARNING FUZZY CONTROL RULES USING GENETIC ALGORITHMS |
| |
Authors: | Fang Jian'an Shao Shihuang |
| |
Institution: | Department of Automation and Electrical Information Engineering |
| |
Abstract: | This paper presents a new genetic algorithms ( GAs) - based method for self-learning fuzzy control rules. An improved GA is used to learn to optimally select the fuzzy membership functions of the linguistic labels in the condition portion of each rule, and to automatically generate fuzzy control actions under each condition. The dynamics of the controlled system is unknown to the GA. The only information for evaluating performance is a failure signal indicating that the controlled system is out of control. We compare its performance with that of other learning methods for the same problem. We also examine the ability of the algorithm to adapt to changing conditions. Simulation results show that such an approach for self-learning fuzzy control rules is both effective and robust. |
| |
Keywords: | genetic algorithm self-learning fuzzy control |
本文献已被 CNKI 等数据库收录! |