Abstract: This paper presents the amalgamation manner and structure of genetic algorithm and evolving fuzzy inference system (EFIS), applies a new EFIS based on GA for dynamic adaptive on-line and off-line learning. An evolving clustering method (ECM) is employed, and new fuzzy rules are created and updated during the operation of the system. Genetic algorithm is applied to optimize the result of ECM and modify the membership functions, calculates the system output by fuzzy inference system.

Key words: Genetic algorithm; Evolving fuzzy inference system; Evolving clustering; Modeling and simulation

摘要： 介绍了遗传算法和进化模糊推理系统的融合方式及结构，应用一种新型的基于遗传算法的进化模糊推理系统动态自适应的在线学习和离线学习。使用进化聚类方法，模糊规则在系统执行过程中进行创建和更新，并且采用遗传算法优化进化聚类的结果，修改成员的隶属度函数，通过模糊推理系统计算系统的输出。

关键词: 遗传算法；进化模糊推理系统；进化聚类；建模与仿真