Prediction of the chaotic time series based on chaotic simulated annealing and support vector machine

【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】

The regression accuracy and generalization performance of the support vector regression(SVR) model depend on a proper setting of its parameters.An optimal selection approach of SVR parameters was put forward based on chaotic simulated annealing algorithm(CSAA),the key parameters C and ε of SVM and the radial basis kernel parameter g were optimized within the global scope.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-4 8.756 ' 10.Simulation results show that the optimal selection approach based on CSAA is available and the CSAA-SVR model can predict the chaotic time series accurately.

【Keywords】

support vector machine;chaotic simulated annealing algorithm;chaotic time series prediction;phase space reconstruction.

References

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Springer Journals Database

Total: 11 articles

  • [1] WANG Ling, ZHENG Dazhong (Department of Automation, Tsinghua University, Beijing 100084, China), Simulated annealing with the state generator based on Cauchy and Gaussian distributions, JOURNAL OF TSINGHUA UNIVERSITY(SCIENCE AND TECHNOLOGY),
  • [2] 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,
  • [3] 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,
  • [4] 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,

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