Abstract
AbstractSeveral 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...
Highlights
Introduction and biomass power generationThe safety of these generation equipments is important guarantee forAs the world's energy increasingly scarce, governments take more attention to the development and utilization of renewable energy, and spent a lot of manpower and ensuring the engineering operation normally
In large and medium-sized water the nonlinear system, and has been successfully used in many research fields3-14. It is attracting more and more conservancy projects, the percentage of gate and hoist attention in water conservancy and hydropower which have been run into the ground reached 68.5%
As for the testing sample mentioned above, the testing parameters, and each basic model has two hidden layers, result of integration model (Fig.7) shows that its correct with 20 nodes attached to one layer and 11 nodes for the rate rises to 78%, being apparently better than that of a other one, besides, the range of initial weight is [-0.3, single one and more stable, either changing the training
Summary
Abstract is 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 g 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 e 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. N Keywords: Hydraulic metal structure, health diagnosing, BP neural network, integration model, bagging U technology. The safe operation of the water resources and hydropower engineering metal structure, an important link material resources to research and develop new energies in the safe operation of the water conservancy, and such as solar energy, wind energy, hydroelectric power. There are more than 87000 reservoirs and multi-layer fuzzy comprehensive evaluation index system structure
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