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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Su, H., Li, Q. (2006). Fuzzy Neural Classifier for Transformer Fault Diagnosis Based on EM Learning. In: Huang, DS., Li, K., Irwin, G.W. (eds) Computational Intelligence. ICIC 2006. Lecture Notes in Computer Science(), vol 4114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-37275-2_29
Download citation
DOI: https://doi.org/10.1007/978-3-540-37275-2_29
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37274-5
Online ISBN: 978-3-540-37275-2
eBook Packages: Computer ScienceComputer Science (R0)