NDLI: Diagnostic Reasoning of Mechanical Fault via Fuzzy Neural Networks

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Diagnostic Reasoning of Mechanical Fault via Fuzzy Neural Networks

Content Provider	Scilit
Author	Yue, Li Xisen, Wen Jin, Qiu
Copyright Year	2020
Description	The method and strategy of combining traditional neural networks with fuzzy analysis technique, which discussed deeply in this paper, are valuable for application of solving the problems of diagnostic reasoning for a variety of mechanical faults. The fuzzy fault diagnosis put into practice by fuzzy processing of input-output of the conventional structure of neural networks. The network weights are trained and optimized by using genetic algorithm. The results of experiment performed on gear couples of NF125 type motor engine demonstrate that the hybrid system of fuzzy neural networks has a good ability of recognizing the states of samples and validity of diagnostic reasoning for border fuzzy situation of fault feature data. Book Name: Industrial and Engineering Applications of Artificial Intelligence and Expert Systems
Related Links	https://api.taylorfrancis.com/content/chapters/edit/download?identifierName=doi&identifierValue=10.1201/9780429332197-58&type=chapterpdf
Ending Page	467
Page Count	5
Starting Page	463
DOI	10.1201/9780429332197-58
Language	English
Publisher	Informa UK Limited
Publisher Date	2020-01-08
Access Restriction	Open
Subject Keyword	Book Name: Industrial and Engineering Applications of Artificial Intelligence and Expert Systems Geological Engineering Optimized Fuzzy Neural Neural Networks with Fuzzy Structure Diagnostic Reasoning
Content Type	Text
Resource Type	Chapter

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