Fault diagnosis of analogue circuits using fuzzy neural network mixed with genetic algorithms

被引：0
|
作者：
Zhu Yanqing [1 ]
He Yigang [1 ]
Liang Gechao [1 ]
机构：
[1] Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Hunan, Peoples R China
来源：
DYNAMICS OF CONTINUOUS DISCRETE AND IMPULSIVE SYSTEMS-SERIES B-APPLICATIONS & ALGORITHMS | 2007年 / 14卷
关键词：
data clustering; fault diagnosis; fuzzy neural network; genetic algorithm;
D O I：
暂无
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
An important issue that arises in the microelectronics and semiconductor industry is the testing of analogue circuits and systems. In this paper, a systematic approach combining neural network, fuzzy reasoning and genetic algorithm is proposed to automate the fault diagnostic process in analogue circuits. The presented fuzzy neural network is developed with the improved fuzzy weighted reasoning method and trained with the clustered samples. And the adjusting of connection weights and optimization of membership functions are performed with genetic algorithms. Then the fuzzy rules can be automatically obtained according to the weights of the trained network. The results of experimental tests show that this method can satisfactorily detect and identify the faults.
引用
收藏
页码：158 / 162
页数：5
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