Algorithms Research in the Application of Lorenz Time Series Prediction


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Prediction of Lorenz Chaotic Time Series is a vital problem in nonlinear dynamics .Support vector machine (SVM) is a kind of novel machine learning methods based on statistical learning theory, which have been provided an efficient algorithm thought in prediction of Chaotic Time Series. This paper combined SVM with neural network which based on the similarity of structure between SVM and RBF Networks, using SVM to obtain the centers of RBF Networks, then to predict the Lorenz Chaotic Time Series. Simulation results show that the effect is better than other methods.



Advanced Materials Research (Volumes 268-270)

Edited by:

Feng Xiong




M. X. Miao and Y. J. Gang, "Algorithms Research in the Application of Lorenz Time Series Prediction", Advanced Materials Research, Vols. 268-270, pp. 1017-1020, 2011

Online since:

July 2011




[1] Vapnik V N 2000 The Nature of Statistical Learning Theory (NewYork : Springer).

[2] Casdagli Nonlinear Prediction of Chaotic Time Series. Physical D , 1989, 53. pp.335-356.

[3] PLATTER Andras. The equivalence of support vector machine and regularization neural networks. Neural Processing Letters. 2002, 15(2). pp.97-104.

[4] Wu Jinpei, Sun Desheng. Model Data Analysis. Beijing: Mechanical Industry Press, 2006. 2.

[5] Feng Guohe, Zhu Siming. Modified SVM and Its Application to Time Series Forecasting. Journal of South China University of Technology(Natural Science Edition), 2005. 33(5). pp.19-22.

[6] Wang Haiyan, Sheng Zhaohan. Choice of the Parameters for the Phase Space Reconstruction of Chaotic Time Series. Journal of Southeast University(Natural Science Edition). 2000. 30(5). pp.113-117.

[7] Li Ying, Jiao Licheng. MECHANISM STUDY ON SOFT COMPUTING PARADIGMS AND SUPPORT VECTOR MACHINE UNDER A UNIFIED FRAME WORK. Pattern Recognition and Artificial Intelligence, 2002. 2(15). pp.172-177.