BP neural network integration model research for hydraulic metal structure health diagnosing
| Autor: | Yong Huang, Chongshi Gu, Kun Yang, Guang Ming Yang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: |
Structure (mathematical logic)
General Computer Science Artificial neural network Computer science business.industry Network structure Sample (statistics) QA75.5-76.95 BP neural network computer.software_genre lcsh:QA75.5-76.95 Computational Mathematics Network integration Electronic computers. Computer science bagging technology Hydraulic metal structure health diagnosing Data mining Artificial intelligence lcsh:Electronic computers. Computer science business integration model computer |
| Zdroj: | International Journal of Computational Intelligence Systems, Vol 7, Iss 6 (2014) International Journal of Computational Intelligence Systems, Vol 7, Iss 6 (2017) |
| ISSN: | 1875-6883 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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