基于小波隶属函数的模糊推理规则优化  

The optimization of fuzzy inference rules based on wavelet membership function

作　　者：金义富[1] 朱庆生[1] 

机构地区：[1]重庆大学计算机学院

出　　处：《微计算机信息》2007年第01Z期293-295,共3页Control & Automation

基　　金：重庆市自然科学基金资助项目(2005BB2224)

摘　　要：隶属函数决定着模糊集的特征,建立小波基函数与隶属函数之间的联系,从而利用小波分析探讨模糊推理的实质,以一种非对称Haar小波基与三角型、梯型隶属函数的对应关系为基础,将小波分析、遗传算法与模糊系统结合,利用遗传算法实现小波隶属函数的训练学习,进而实现模糊推理规则的优化。Characteristics of fuzzy set are decided to its membership function （MF）. The relation of wavelet basis and MF is built up and essentials of fuzzy inference are revealed by means of wavelet analysis in this paper. Based on corresponding relationship of trian- gle, trapezoid MF and unsymmetrical Haar wavelet basis and combined wavelet analysis, genetic algorithm （GA） and fuzzy system, the self learning of wavelet membership function is realized through GA,furthermore the optimization of fuzzy inference rules is come true.

关 键 词：非对称Haar小波 隶属函数 模糊推理 遗传算法 优化 

分 类 号：O159[理学—数学]