Analog circuits fault diagnosis using fuzzy neural network mixed with genetic algorithms

被引：0

作者：

He, YG ^{[1
]}

Liang, GC ^{[1
]}

机构：

[1] Hunan Univ, Fac Elect & Informat Engn, Changsha 410082, Peoples R China

来源：

7TH WORLD MULTICONFERENCE ON SYSTEMICS, CYBERNETICS AND INFORMATICS, VOL XV, PROCEEDINGS: COMMUNICATION, CONTROL, SIGNAL AND OPTICS, TECHNOLOGIES AND APPLICATIONS | 2003年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A fault identification approach for nonlinear analogue systems is presented. A fuzzy neural network is developed based on the improving fuzzy weighted reasoning method. ne training of network weights and optimization of membership functions are conducted employing genetic algorithms. Fuzzy rules can be realized through the refresh of the weights of the neural network. The availability of the method is examined by simulated test examples.(1)

引用

页码：310 / 315

页数：6

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