Full-Text Search:
Home|Journal Papers|About CNKI|User Service|FAQ|Contact Us|中文
《Electrical Measurement & Instrumentation》 2015-14
Add to Favorite Get Latest Update

Fuzzy neural network expert system for fault diagnosis in power lithium battery application

Wang Yihui;Jiang Changhong;School of Electrical & Electronic Engineering,Changchun University of Technology;Changchun Vocational Institute of technology;  
The cause of power lithium battery failure has a certain complexity and uncertainty. To this end,this paper proposes a fault diagnosis expert system based on fuzzy neural network. This method combines the advantages of fuzzy mathematics,neural network and expert system. Using fuzzy mathematics can be blurred to characterize the membership degree of the fault symptoms,the neural network has good self-learning ability,the expert system has strong reasoning ability,All three together,that is not only to improve the accuracy of the system and operability,but also meet the requirement of the intelligent and automatic diagnosis for faults. The test results show that the method can accurately judge the fault in the system,it not only to increase the accuracy of fault detection to 0. 001,control the prediction error at between 1% and 8%,but also shorten the testing time. This method improves the self-adaptive ability of the power lithium batteries,the independent learning ability,and puts forward a new scientific and efficient method for power lithium battery fault diagnosis.
【Fund】: 吉林省科技发展计划项目(20140204029sf)
【CateGory Index】: TM912
Download(CAJ format) Download(PDF format)
CAJViewer7.0 supports all the CNKI file formats; AdobeReader only supports the PDF format.
©2006 Tsinghua Tongfang Knowledge Network Technology Co., Ltd.(Beijing)(TTKN) All rights reserved