Hu Yuxia College of Electric Engineering Zhengzhou University Zhengzhou,China Zhang Hongtao Institute of Electric power North China Institute of Water Conservancy and Hydroelectric Power Zhengzhou,China
For support vector regression(SVR),the setting of key parameters is very important,which determines the regression accuracy and generalization performance of SVR model.In this paper,an optimal selection approach for SVR parameters was put forward based on mutative scale optimization algorithm(MSCOA),the key parameters C and ε of SVM and the radial basis kernel parameter g were optimized within the global scopes.The support vector regression model was established for chaotic time series prediction by using the optimum parameters.The time series of Lorenz system was used to testify the effectiveness of the model.The root mean square error of prediction reached 3 RMSE3.0335 10 = '.Simulation results show that the optimal selection approach based on MSCOA is an effective approach and the MSCOA-SVR model has a good performance for chaotic time series forecasting.
support vector machine;Mutative scale chaos optimization algorithm;chaotic time series prediction;phase space reconstruction,parameter selection.
To explore the background and basis of the node document
Documents that have the similar content to the node document