Analog Circuit Soft Fault Diagnosis Based on Chaotic Neural Network

DOI

10.2991/mcae-16.2016.48How to use a DOI?

Keywords

analog circuit; soft fault diagnosis; chaotic; neural network; fuzzy clustering

Abstract

In order to solve the fault feature redundancy problem in analog circuit fault diagnosis, a method of fault diagnosis is presented in this paper. This approach using the method of wavelet decomposition and fuzzy clustering on the fault signals to obtain test matrix; then inputted the test matrix into the neural network for fault diagnosis. The approach are combined the chaotic motion's ergodic, randomness and sensitive of the initial value to optimize the neural network for making the network have a better learning ability and have a more faster convergence speed to improve the efficiency of fault diagnosis. The simulation results verify the effectiveness of this approach.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Meirong Liu
AU  - Li Zeng
AU  - Liwei Zhang
AU  - Yigang He
PY  - 2016/07
DA  - 2016/07
TI  - Analog Circuit Soft Fault Diagnosis Based on Chaotic Neural Network
BT  - Proceedings of the 2016 International Conference on Mechatronics, Control and Automation Engineering
PB  - Atlantis Press
SP  - 201
EP  - 205
SN  - 2352-5401
UR  - https://doi.org/10.2991/mcae-16.2016.48
DO  - 10.2991/mcae-16.2016.48
ID  - Liu2016/07
ER  -