Underground drainage system based on artificial intelligence control

Wen, Zhenhe; Guochao, Li

doi:10.1109/iccsn.2011.6014364

Cited by 1 publication

(2 citation statements)

References 4 publications

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“…(5) Record the number of studied samples p. If p < P, turn to step 2 continue to computing; If p = P, turn to step 6. (6) According to the weight correct formula (6), (8), and (10) ,correct weight or threshold of each layer. (7) According to the new weight, recalculate…”

Section: U N R E G I S T E R E Dmentioning

confidence: 99%

“…The creation and research of artificial neural network theory provide a powerful tool for the study of the nonlinear system, and has been successfully used in many research fields [3][4][5][6][7][8][9][10][11][12][13][14] . It is attracting more and more attention in water conservancy and hydropower engineering safety evaluation field, and has been widely applied [15][16][17][18] .…”

Section: Introductionmentioning

confidence: 99%

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

Yang¹,

Gu²,

Huang³

et al. 2014

IJCIS

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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.

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Section: U N R E G I S T E R E Dmentioning

confidence: 99%