基于差分进化算法的混沌时间序列预测模型参数组合优化

张文专; 龙文; 焦建军

doi:10.7498/aps.61.220506

摘要

为了提高混沌时间序列预测模型的预测精度, 提出一种基于差分进化(DE)算法的相空间重构和最小二乘支持向量机(LSSVM)模型参数组合优化方法. 该方法将相空间重构参数和LSSVM预测模型参数进行组合作为差分进化算法的个体, 以混沌时间序列预测精度作为个体的适应度函数, 通过循环迭代获得最优参数组合. 几个混沌时间序列的仿真实验结果表明, 与传统的优化方法相比, 参数组合优化方法具有更高的预测精度.

关键词:

Abstract

To improve the prediction accuracy of the chaotic time series prediction model, a composite optimization method of the differential evolution (DE) algorithm that is based on the phase space reconstruction and least square supported vector machine (LSSVM), is proposed. The phase space parameters and LSSVM model parameters are taken as differential evolution algorithm individuals while the prediction accuracy of the chaotic time series is used as the evaluation function of DE algorithm. The optimal parameters are obtained by mutation, crossover, and selection operators of DE algorithm. Several numerical simulation results show that not only four parameters are determined at the same time, but also the performance of chaotic time series prediction is improved.

Keywords:

作者及机构信息

1.
贵州财经大学, 贵州省经济系统仿真重点实验室, 贵阳 550004

基金项目: 国家自然科学基金(批准号: 10961008)资助的课题.

Authors and contacts

1.
Guizhou Key Laboratory of Economics System Simulation, Guizhou University of Finance and Economics, Guiyang 550004, China

Funds: Project supported by the National Natural Science Foundation of China (Grant No. 10961008).

参考文献

[1]	Peng S G, Yu S M 2009 Chin. Phys. B 18 3758
[2]	Xiang C S, Yuan Z M, Zhou Z Y 2011 Information and Control 40 673(in Chinese) [向昌盛, 袁哲明, 周子英 2011 信息与控制 40 673]
[3]	Fraser A M 1989 IEEE Trans. Inform. Theory 35 245
[4]	Albano A M 1988 Phys. Rev. A 38 3017
[5]	Kugiumtzis D 1996 Physica D 95 13
[6]	Kim H S, Eykholt R Salas J D 1999 Physica D 127 48
[7]	Ataei M, Lohmann B, Khaki-Sedigh A, Lucas C 2004 Chaos, Solitons and Fractals 19 1131
[8]	Chen D Y, Liu Y, Ma S Y 2012 Acta Phys. Sin. 61 100501 (in Chinese) [陈帝伊, 柳烨, 马孝义2012 物理学报 61 100501]
[9]	Ma Q L, Zheng Q L, Peng H, Zhong T W, Qin J W 2008 Chin. Phys. B 17 536
[10]	Wang Y S, Sun J, Wang C J, Fan H D 2008 Acta Phys. Sin. 57 6120(in Chinese) [王永生, 孙瑾, 王昌金, 范洪达 2008 物理学报 57 6120]
[11]	Zhang J F, Hu S S 2008 Acta Phys. Sin. 57 2708 (in Chinese) [张军峰, 胡寿松 2008 物理学报 57 2708]
[12]	Ye M Y, Wang X D 2004 Chin. Phys. 13 454
[13]	Long W, Liang X M, Long Z Q, Li Z H 2011 J. Central South University 42 3408 (in Chinese) [龙文, 梁昔明, 龙祖强, 李朝辉中南大学学报 42 3408]
[14]	Zhang Q R, Ma Q L, Peng H 2010 Acta Phys.Sin. 59 7623(in Chinese) [张春涛, 马千里, 彭宏 2010 物理学报 59 7623]
[15]	Storn R, Price K 1997 J. Global Optim. 11 341
[16]	Wang Y, Cai Z X, Zhang Q F 2011 IEEE Trans. Evolu. Comput. 15 55
[17]	Ren Z W, San Y 2007 Acta Elec. Sin. 35 269 (in Chinese) [任子武, 伞冶 2007 电子学报 35 269]

施引文献

[1]	Peng S G, Yu S M 2009 Chin. Phys. B 18 3758
[2]	Xiang C S, Yuan Z M, Zhou Z Y 2011 Information and Control 40 673(in Chinese) [向昌盛, 袁哲明, 周子英 2011 信息与控制 40 673]
[3]	Fraser A M 1989 IEEE Trans. Inform. Theory 35 245
[4]	Albano A M 1988 Phys. Rev. A 38 3017
[5]	Kugiumtzis D 1996 Physica D 95 13
[6]	Kim H S, Eykholt R Salas J D 1999 Physica D 127 48
[7]	Ataei M, Lohmann B, Khaki-Sedigh A, Lucas C 2004 Chaos, Solitons and Fractals 19 1131
[8]	Chen D Y, Liu Y, Ma S Y 2012 Acta Phys. Sin. 61 100501 (in Chinese) [陈帝伊, 柳烨, 马孝义2012 物理学报 61 100501]
[9]	Ma Q L, Zheng Q L, Peng H, Zhong T W, Qin J W 2008 Chin. Phys. B 17 536
[10]	Wang Y S, Sun J, Wang C J, Fan H D 2008 Acta Phys. Sin. 57 6120(in Chinese) [王永生, 孙瑾, 王昌金, 范洪达 2008 物理学报 57 6120]
[11]	Zhang J F, Hu S S 2008 Acta Phys. Sin. 57 2708 (in Chinese) [张军峰, 胡寿松 2008 物理学报 57 2708]
[12]	Ye M Y, Wang X D 2004 Chin. Phys. 13 454
[13]	Long W, Liang X M, Long Z Q, Li Z H 2011 J. Central South University 42 3408 (in Chinese) [龙文, 梁昔明, 龙祖强, 李朝辉中南大学学报 42 3408]
[14]	Zhang Q R, Ma Q L, Peng H 2010 Acta Phys.Sin. 59 7623(in Chinese) [张春涛, 马千里, 彭宏 2010 物理学报 59 7623]
[15]	Storn R, Price K 1997 J. Global Optim. 11 341
[16]	Wang Y, Cai Z X, Zhang Q F 2011 IEEE Trans. Evolu. Comput. 15 55
[17]	Ren Z W, San Y 2007 Acta Elec. Sin. 35 269 (in Chinese) [任子武, 伞冶 2007 电子学报 35 269]

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