Application of Neural Network Integration in Fault Diagnosis
Wei Xiong*, 1, 2, Xuehui Xian2, Lijing Zhang2
Identifiers and Pagination:Year: 2014
First Page: 81
Last Page: 84
Publisher Id: TOMEJ-8-81
Article History:Received Date: 11/11/2013
Revision Received Date: 11/02/2014
Acceptance Date: 03/03/2014
Electronic publication date: 21/3/2014
Collection year: 2014
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.
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.