NDLI: Gradient genetic algorithm-based performance fault diagnosis model

Content Provider	IEEE Xplore Digital Library
Author	Wei Zhang Jiang Zhu Li Fang Kong
Copyright Year	2011
Description	Author affiliation: Xuzhou Air Force College, Jiangsu, 221002, China (Li Fang Kong) \|\| Xuzhou Air Force College, Jiangsu, 221008, China (Wei Zhang; Jiang Zhu)
Abstract	This paper, in order to reduce fault and improve ratio of recognition, build adaptive neural network-based fuzzy inference system (ANFIS), which was applied to build a fault diagnosis model of automobile engine, adopts the method of information fusion in entropy method to optimize the input interface. To reduce the impact of excessive parameters on classification accuracy and cost, it also raises an asynchronous parallel particle swarm optimization method applied to the selection of feature subset. By using gradient descent genetic algorithm and optimization of system parameters of neutral network learning algorithm, so as to speed up learning. Through verification of the build diagnosis model with data of engine tests, it has been found that the recognition accuracy attain to 97.39%, training error falling to 0.001702. The experiment indicates that gradient descent genetic algorithm is a fast algorithm that can support the local optimization of individual chromosome and the global optimization of chromosomes in a group.
Starting Page	3059
Ending Page	3062
File Size	372750
Page Count	4
File Format	PDF
ISBN	9781457705359
e-ISBN	9781457705366
DOI	10.1109/AIMSEC.2011.6010844
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-08
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Fault diagnosis Adaptation models Performance parameter Biological cells Optimization Adaptive neural fuzzy interference system Genetic algorithms Testing Gradient descent genetic algorithm
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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