By Topic

On the application of artificial neural networks to fault diagnosis in analog circuits with tolerances

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Deng Ying ; Coll. of Electr. & Inf. Eng., Hunan Univ., Changsha, China ; He Yigang

This paper proposes a method for analog fault diagnosis by adopting neural networks. The primary focus of the paper is to provide robust diagnosis using a mechanism to deal with the problem of component tolerances and to reduce the testing time. The proposed approach is based on the k-fault diagnosis method and artificial backward propagation neural network, which is shown to be capable of robust diagnosis of analog circuits with tolerances

Published in:

Signal Processing Proceedings, 2000. WCCC-ICSP 2000. 5th International Conference on  (Volume:3 )

Date of Conference:

2000