Study on Prediction of Power Allocation for the Double-Wheel Trench Cutter Control System Based on Extreme Learning Machine Method
| Autor: | Yougang Sun, Yuchen Hu, Haiyan Qiang, Wanli Li, Guanyuan Li |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Artificial neural network
Computer science 020209 energy 02 engineering and technology Power (physics) Set (abstract data type) Control theory Control system 0202 electrical engineering electronic engineering information engineering Torque 020201 artificial intelligence & image processing Radial basis function ComputingMethodologies_COMPUTERGRAPHICS Extreme learning machine Variable (mathematics) |
| Zdroj: | ICARM Web of Science |
| DOI: | 10.1109/icarm.2019.8834246 |
| Popis: | It is uneasy to obtained the accurate mathematical model of a double-wheel trench cutter owing to the dynamics, uncertainty and complexity of the cutter and the working environment. In order to solve this problem, an approach based on neural network was applied to develop a prediction model. The torques and feeding depth of each milling wheel were set as the input variable and while the power of milling pumps was set as the output variable. The comparisons of the simulation and experimental prediction of the extreme learning machine method have been presented and the radial basis function method was utilized as a comparison group. The results obtained from the extreme learning machine method are satisfactory and superior to the radial basis function method. |
| Databáze: | OpenAIRE |
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