BP neural network integration model research for hydraulic metal structure health diagnosing

DOI

10.1080/18756891.2014.966999How to use a DOI?

Keywords

Hydraulic metal structure, health diagnosing, BP neural network, integration model, bagging technology

Abstract

Several potential network structures are chosen to do a large number of experimental analysis, historical data is divided into training sample and testing sample, and the corresponding neural network model is established with BP learning algorithm. After checking the testing sample, a superior network integration model which can be applied for hydraulic metal structure health grade diagnosing is determined. By plenty of experimental tests and verification analysis, it is concluded that the two-hidden-layer neural network model suits hydraulic metal structure health diagnosing better. As for the gate health diagnosing, based on Bagging technology, the BP neural network integration model for hydraulic metal structure health diagnosing is researched and constructed. The analysis of the sample showed that its accuracy rate (78%) is obviously better than the single neural network model(67%). The BP neural network integration model will work together with the FAHP model the author studied, that can make the diagnosis results more reasonable and reliable.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Guangming Yang
AU  - Chongshi Gu
AU  - Yong Huang
AU  - Kun Yang
PY  - 2014
DA  - 2014/12/01
TI  - BP neural network integration model research for hydraulic metal structure health diagnosing
JO  - International Journal of Computational Intelligence Systems
SP  - 1148
EP  - 1158
VL  - 7
IS  - 6
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2014.966999
DO  - 10.1080/18756891.2014.966999
ID  - Yang2014
ER  -