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International Symposium on Neural Networks

ISNN 2007: Advances in Neural Networks – ISNN 2007 pp 570–579Cite as

Probabilistic Neural Network Based Method for Fault Diagnosis of Analog Circuits

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4493))

Abstract

A novel method for fault diagnosis of analog circuits with tolerance based on wavelet packet decomposition (WP) and probabilistic neural networks (PNN) is proposed in the paper. The fault feature vectors are extracted after feasible domains on the basis of WP decomposition of responses of a circuit is solved. Then by fusing various uncertain factors into probabilistic operations, parameters and structures of PNNs for diagnose faults are obtained based on genetic optimization method leading to best detection of faults. Finally, simulations indicated that PNN classifiers can correctly 7% more than BPNN of the test data associated with our sample circuits.

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Authors and Affiliations

  1. College of electric & information engineering, Hunan University, Changsha 410082, China

    Yanghong Tan, Yigang He & Meirong Liu

Authors
  1. Yanghong Tan
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  2. Yigang He
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  3. Meirong Liu
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Editor information

Derong Liu Shumin Fei Zengguang Hou Huaguang Zhang Changyin Sun

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© 2007 Springer Berlin Heidelberg

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Tan, Y., He, Y., Liu, M. (2007). Probabilistic Neural Network Based Method for Fault Diagnosis of Analog Circuits. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_71

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  • DOI: https://doi.org/10.1007/978-3-540-72395-0_71

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72394-3

  • Online ISBN: 978-3-540-72395-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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