DocumentCode :
2779929
Title :
Expert Systems for Fault Diagnosis Integrating Neural Network and Fuzzy Inference
Author :
Hong, Guang ; Chen, Xin ; Xue, Xuedong ; Zhang, Shuai
Author_Institution :
Dept. Three, Wuhan Mech. Technol. Coll., Wuhan, China
Volume :
1
fYear :
2011
fDate :
24-25 Sept. 2011
Firstpage :
245
Lastpage :
248
Abstract :
In order to improve the accuracy of diagnosis to satisfy the maintenance of weapon equipment, a kind of expert system (ES) is used in this paper. The system is integrated into neural network (NN) and fuzzy inference. The basic system structure diagrams adopt the frame of expert systems. ES is logic inference part and responsible for symbol processing. The status parameters are described by fuzzy theory and the structure model is built with fuzzy directed graph. The fuzzy logic inference provides an appropriate knowledge representation method to depict fuzzy knowledge. NN is responsible for numerical value calculation. The learning ability of NN can partially or entirely resolve the bottleneck problem of knowledge acquisition. The results indicate the validity and rationality of the model and the method.
Keywords :
diagnostic expert systems; directed graphs; fault diagnosis; fuzzy reasoning; fuzzy set theory; knowledge acquisition; knowledge representation; learning (artificial intelligence); maintenance engineering; military computing; weapons; expert systems; fault diagnosis; fuzzy directed graph; fuzzy knowledge; fuzzy logic inference; fuzzy theory; knowledge acquisition; knowledge representation method; neural network learning; symbol processing; system structure diagrams; weapon equipment maintenance; Artificial neural networks; Circuit faults; Databases; Expert systems; Fault diagnosis; Knowledge engineering; Training; expert system; fault diagnosis; fuzzy theory; genetic algorithm;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Technology, Computer Engineering and Management Sciences (ICM), 2011 International Conference on
Conference_Location :
Nanjing, Jiangsu
Print_ISBN :
978-1-4577-1419-1
Type :
conf
DOI :
10.1109/ICM.2011.170
Filename :
6113402
Link To Document :
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