Fuzzy neural classifier for transformer fault diagnosis based on EM learning

Authors:
Hongsheng Su;Qunzhan Li
Affiliations:
School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China;School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China
Venue:
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Year:
2006

Citing 2
Cited 2

Neural networks for control systems: a survey

Automatica (Journal of IFAC)
Adaptive control of nonlinear multivariable systems using neural networks

Neural Networks

Transformer fault diagnosis method based on rough set and Bayesian otimal classifier

CISST'09 Proceedings of the 3rd WSEAS international conference on Circuits, systems, signal and telecommunications
Transformer fault diagnosis based on reasoning integration of rough set and fuzzy set and Bayesian optimal classifier

WSEAS Transactions on Circuits and Systems

Quantified Score

Hi-index	0.00

Visualization

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.