Title :
Voting Algorithm of Fuzzy ARTMAP and Its Application to Fault Diagnosis
Author :
Tang, Zhiyong ; Yan, Xiang´an
Author_Institution :
Tianjin Univ., Tianjin
Abstract :
Simplified fuzzy ARTMAP (SFAM) is a simplification of fuzzy ARTMAP (FAM) by reducing the computational overhead and architectural redundancy. SFAM is powerful neural network in the application of prediction and classifier. Individual SFAM performance depends on the ordering of training sample presentation. A multiple classifier combination scheme is proposed in this paper to obtain reliable and accurate fault diagnosis. SFAMs input the data vectors in the complement code format and output fault diagnosis results. The sum rule voting algorithm combines the SFAMs outputs and generates final conclusion. A weight is assigned to each SFAM according to its historical achievements. Case study has shown that the proposed method is effective in fault diagnosis application.
Keywords :
ART neural nets; fault diagnosis; fuzzy neural nets; learning (artificial intelligence); maintenance engineering; mechanical engineering computing; pattern classification; SFAM neural network; architectural redundancy reduction; computational overhead reduction; fault diagnosis; mechanical component faults; multiple classifier combination scheme; simplified fuzzy ARTMAP; sum rule voting algorithm; supervised learning mechanism; Artificial neural networks; Fault diagnosis; Fuzzy logic; Fuzzy neural networks; Fuzzy systems; Mechanical engineering; Neurons; Redundancy; Subspace constraints; Voting;
Conference_Titel :
Fuzzy Systems and Knowledge Discovery, 2007. FSKD 2007. Fourth International Conference on
Conference_Location :
Haikou
Print_ISBN :
978-0-7695-2874-8
DOI :
10.1109/FSKD.2007.611