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
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;
Conference_Titel :
Circuits and Systems, 1994., Proceedings of the 37th Midwest Symposium on
Conference_Location :
Lafayette, LA
Print_ISBN :
0-7803-2428-5
DOI :
10.1109/MWSCAS.1994.519064
