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Fuzzy Neural Classifier for Transformer Fault Diagnosis Based on EM Learning

  • Hongsheng Su
  • Qunzhan Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4114)

Abstract

A novel fuzzy neural classifier and learning algorithm are proposed based on EM learning in this paper. The method firstly applies rough set of its information measurement ability to evaluate system parameters importance. Then, based on EM learning the unknown parameters of fuzzy member functions are estimated. Then a fuzzy neural classifier based on EM algorithm is generated. The research indicates that the proposed network possesses higher diagnosis precision and speed as well as excellent anti-interference abilities, and is an ideal pattern classifier. In the end, a practical application in transformer fault diagnosis shows the availability of the method.

Keywords

Fault Diagnosis Wavelet Neural Network Importance Factor Input Object Gauss Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hongsheng Su
    • 1
  • Qunzhan Li
    • 1
  1. 1.School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031China

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