摘要
结合进化学习分类器的密歇根和匹兹堡方法的优点，首次将对单条控制规则的评价引入模糊逻辑控制器（ＦＬＣ）的进化过程中，解决了匹兹堡类型的学习分类器系统“强化信息的带宽窄”的问题，实现了对ＦＬＣ在控制器级和规则级的同时进化．控制器的控制规则数目也可以自由变化．实验结果表明新方法有较高的效率，优化的模糊控制器结构简单。
Optimization of FLC (fuzzy logic controller) by genetic algorithm is a promising research area. First, rule based FLC is reviewed briefly in the paper. Then, the merits of Pittsburgh approach and Michigan approach are combined in the new FLC's evolving algorithm, and the method of evaluating individual fuzzy rule is introduced into the evolving procedure. FLC and fuzzy rules can be optimized simultaneously in the algorithm. The number of fuzzy rules is variable together with the fixed length of chromosome in the algorithm. Experiments show that the algorithm is efficiency and the optimized FLC is excellent.
出处
《计算机学报》
EI
CSCD
北大核心
1999年第6期662-667,共6页
Chinese Journal of Computers
基金
国家八六三高技术研究发展计划
