REVIEW ARTICLE
Application of Neural Network Integration in Fault Diagnosis
Wei Xiong*, 1, 2, Xuehui Xian2, Lijing Zhang2
1 School of Control and Computer Engineering, North China Electric Power University Baoding, 071003, China
2 Information and Network Management Center, North China Electric Power University Baoding, 071003, China
Article Information
Identifiers and Pagination:
Year: 2014Volume: 8
First Page: 81
Last Page: 84
Publisher Id: TOMEJ-8-81
DOI: 10.2174/1874155X01408010081
Article History:
Received Date: 11/11/2013Revision Received Date: 11/02/2014
Acceptance Date: 03/03/2014
Electronic publication date: 21/3/2014
Collection year: 2014
© 2014 Xiong et al
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
According to the generation methods of individual neural network and the methods of generating conclusions from integrated neural network, an effective neural network integration system can be constructed. An optimization method for neural network integration is proposed. In the generation of individuals in the network integration, a variety of genetic algorithms and particle swarm optimization algorithm are used to train individual networks, thus to improve the precision of network members and reduce the correlation among the network members; in the conclusion generation, weight of the individual neural network is dynamically determined. The simulation results show that the effectiveness and feasibility of the method in fault diagnosis.
Keywords: Dynamic selectivity, fault diagnosis, kernel fuzzy C-means clustering integration, neural network integration, particle swarm optimization algorithm, particle swarm optimization algorithm.
