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A NEURAL NETWORK BASED FAULT FUZZY DIAGNOSTIC SYSTEM
作者姓名:吴蒙  何振亚
作者单位:Department of Radio Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210003,Department of Radio Engineering,Southeast University,Nanjing 210018
基金项目:Supported by the Climbing Programme-National Key Project for Fundamental Research in China, Grant NSC92097
摘    要:A fault fuzzy diagnostic system(FFDS) based on neural network and fuzzy logic hybrid is proposed. FFDS consists of two modes: a fuzzy inference mode and a rule learning mode. The fuzzy inference rules are stored in the memory layer. The excitation levels of the memory neurons reflect the matching degrees between the input vectors and the prototype rules. In the rule learning mode, the rules can be produced automatically through the cluster process. As an application case of this diagnostic system, the fault diagnosis experiment of the rotating axis is simulated.


A neural network based fault fuzzy diagnostic system
Wu Meng,Feng Guangzeng,He Zhenya.A NEURAL NETWORK BASED FAULT FUZZY DIAGNOSTIC SYSTEM[J].Journal of Electronics,1994,11(3):201-207.
Authors:Wu Meng  Feng Guangzeng  He Zhenya
Affiliation:(1) Department of Radio Engineering, Nanjing University of Posts and Telecommunications, 210003 Nanjing;(2) Department of Radio Engineering, Southeast University, 210018 Nanjing
Abstract:A fault fuzzy diagnostic system(FFDS) based on neural network and fuzzy logic hybrid is proposed. FFDS consists of two modes: a fuzzy inference mode and a rule learning mode. The fuzzy inference rules are stored in the memory layer. The excitation levels of the memory neurons reflect the matching degrees between the input vectors and the prototype rules. In the rule learning mode, the rules can be produced automatically through the cluster process. As an application case of this diagnostic system, the fault diagnosis experiment of the rotating axis is simulated.
Keywords:Neural networks  Fuzzy inference  Expert knowledge  Fault diagnosis
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