Multiple hard fault classification in analog circuits using learning vector quantization neural networks
Joint Authors
al-Jamal, M. A.
Abu al-Yazid, M. F.
Source
Issue
Vol. 4, Issue 2 (31 Jul. 1999), pp.137-153, 17 p.
Publisher
Al al-Bayt University Deanship of Academic Research and Graduate Studies
Publication Date
1999-07-31
Country of Publication
Jordan
No. of Pages
17
Main Subjects
Information Technology and Computer Science
Topics
Abstract EN
A new neural network-based fault classification strategy for hard multiple faults in analog circuits is proposed The magnitude of the' harmonics of the Fourier components of the circuit response at different lest nodes due to a sinusoidal input signal are first measured or simulated A selection criterion for determining the best components that describe the circuit behavior under fault-free (nominal) and fault situations is presented An algorithm that estimates the overlap between different faults in the measurement space is also introduced I he learning vector quantization neural network is then effectively named to classify circuit faults Performance measures reveal very high classification accuracy in both naming and testing stages A detailed example which demonstrates the proposed strategy, is described.
American Psychological Association (APA)
al-Jamal, M. A.& Abu al-Yazid, M. F.. 1999. Multiple hard fault classification in analog circuits using learning vector quantization neural networks. Al-Manarah،Vol. 4, no. 2, pp.137-153.
https://search.emarefa.net/detail/BIM-169140
Modern Language Association (MLA)
al-Jamal, M. A.& Abu al-Yazid, M. F.. Multiple hard fault classification in analog circuits using learning vector quantization neural networks. Al-Manarah Vol. 4, no. 2 (Jul. 1999), pp.137-153.
https://search.emarefa.net/detail/BIM-169140
American Medical Association (AMA)
al-Jamal, M. A.& Abu al-Yazid, M. F.. Multiple hard fault classification in analog circuits using learning vector quantization neural networks. Al-Manarah. 1999. Vol. 4, no. 2, pp.137-153.
https://search.emarefa.net/detail/BIM-169140
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 152-153
Record ID
BIM-169140