Electromechanical equipment state forecasting based on genetic algorithm – support vector regression

► Electromechanical equipment state forecasting model is established by using genetic algorithm-support vector regression. ► Genetic algorithm is employed to choose the training parameters of support vector regression. ► Leave-one-out cross-validation(LOOCV) is adopted to evaluate the fitness of the...

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Vydáno v:Expert systems with applications Ročník 38; číslo 7; s. 8399 - 8402
Hlavní autoři: Huang, Ji, Bo, Yucheng, Wang, Huiyuan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.07.2011
Témata:
Electromechanical equipments
Expert systems
Forecasting
Genetic algorithm
Genetic algorithms
Industrial smokes and gas turbine
Mathematical analysis
Mathematical models
Prediction
Regression
State vectors
Support vector machine
Support vector machines
Vectors (mathematics)
Support vector machine
Electromechanical equipments
Genetic algorithm
Industrial smokes and gas turbine
Prediction
ISSN:0957-4174, 1873-6793
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Shrnutí:► Electromechanical equipment state forecasting model is established by using genetic algorithm-support vector regression. ► Genetic algorithm is employed to choose the training parameters of support vector regression. ► Leave-one-out cross-validation(LOOCV) is adopted to evaluate the fitness of the training parameters of support vector regression. Prediction of electromechanical equipments state nonlinear and non-stationary condition effectively is significant to forecast the lifetime of electromechanical equipments. In order to forecast electromechanical equipments state exactly, support vector regression optimized by genetic algorithm is proposed to forecast electromechanical equipments state. In the model, genetic algorithm is employed to choose the training parameters of support vector machine, and the SVR forecasting model of electromechanical equipments state with good forecasting ability is obtained. The proposed forecasting model is applied to the state forecasting for industrial smokes and gas turbine. The experimental results demonstrate that the proposed GA-SVR model provides better prediction capability. Therefore, the method is considered as a promising alternative method for forecasting electromechanical equipments state.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2011.01.033