Neural network-based analog fault diagnosis using testability analysis

被引：32
|
作者：
Cannas, B
Fanni, A
Manetti, S
Montisci, A
Piccirilli, MC
机构：
[1] Univ Cagliari, Dept Elect & Elect Engn, I-09123 Cagliari, Italy
[2] Univ Florence, Dept Elect & Telecommun, I-50139 Florence, Italy
来源：
NEURAL COMPUTING & APPLICATIONS | 2004年 / 13卷 / 04期
关键词：
diagnosis; neural networks; analog circuits; testability analysis;
D O I：
10.1007/s00521-004-0423-2
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 diagnosis procedure for analog linear circuits is presented. It uses an off-line trained neural network as a classifier. The innovative aspect of the proposed approach is the way the information provided by testability and ambiguity group determination is exploited when choosing the neural network architecture. The effectiveness of the proposed approach is shown by comparing with similar work that has already appeared in the literature.
引用
收藏
页码：288 / 298
页数：11
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