Chaos optimization method of SVM parameters selection for chaotic time series forecasting

【Author】

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

【Abstract】

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.

【Keywords】

support vector machine;Mutative scale chaos optimization algorithm;chaotic time series prediction;phase space reconstruction,parameter selection.

References

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Total: 12 articles

  • [1] ZHANG Zhi-sheng1,SUN Ya-ming1,HE Yun-peng2(1.Tianjin University,Tianjin 300072,China;2.Tianjin Electric Power Bureau,Tianjin 300010,China), A new approach of STLF based on combination of phase space reconstruction theory and optimal recursive neural networks, Electric Power,
  • [2] LIU Qing-kun QUE Pei-wen FEI Chun-guo SONG Shou-peng1. Institute of Automatic Detection, Shanghai Jiaotong University, Shanghai 200030, P.R. China 2. Intelligent Engineering Laboratory, Department of Automation, Shanghai Jiaotong University, Shanghai 200030, P.R. China, Model selection for SVM using mutative scale chaos optimization algorithm, Journal of Shanghai University,
  • [3] Huang Qiaoling Xie Weibo(School of Information Science and Technology,Huaqiao University,Quanzhou 362021,Fujian,China), THE PREDICTION OF SHORTTERM EXCHANGE RATE BASED ON THE PHASE SPACE RECONSTRUCTION AND KALMAN, Computer Applications and Software,
  • [4] SHAN Zhi-chao1,LIN Chun-sheng1,XIANG Qian2(1.Dept.of Weaponry Engineering,Naval Univ.of Engineering,Wuhan 430033,China;2.Naval Deputy of 701 Institute Wuhan 430033,China), Chaos Prediction of Wave Hydrodynamic Pressure Signals Based on Local Support Vectors Machine, Journal of System Simulation,

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