DocumentCode
2766383
Title
Automatically generated rules and membership functions for a neural fuzzy-based fault classifier
Author
Wu, Chwan-Hwa ; Li, Chihwen Chris
Author_Institution
Dept. of Electr. Eng., Auburn Univ., AL, USA
Volume
2
fYear
1994
fDate
3-5 Aug 1994
Firstpage
1377
Abstract
A new learning algorithm for an adaptive neural fuzzy (NF) system is proposed to automatically generate fuzzy rules as well membership functions. This adaptive neural fuzzy system is used for classifying faults in a power system. Remarkable results using this fuzzy fault classifier are reported in this paper. Furthermore, a fuzzy chip is used as the fuzzy classifier to achieve a low-cost real-time implementation
Keywords
adaptive systems; fault diagnosis; fuzzy neural nets; knowledge based systems; learning (artificial intelligence); power system analysis computing; adaptive neural fuzzy system; automatically generated rules; fuzzy chip; fuzzy rules; learning algorithm; membership functions; neural fuzzy-based fault classifier; power system; real-time implementation; Adaptive systems; Biological neural networks; Fuzzy logic; Fuzzy neural networks; Fuzzy reasoning; Fuzzy sets; Fuzzy systems; Noise measurement; Pattern recognition; Power system faults; Subspace constraints;
fLanguage
English
Publisher
ieee
Conference_Titel
Circuits and Systems, 1994., Proceedings of the 37th Midwest Symposium on
Conference_Location
Lafayette, LA
Print_ISBN
0-7803-2428-5
Type
conf
DOI
10.1109/MWSCAS.1994.519064
Filename
519064
Link To Document