Prediction of multivariable chaotic time series using optimized extreme learning machine

Gao Guang-Yong; Jiang Guo-Ping

doi:10.7498/aps.61.040506

Abstract

A prediction algorithm of multivariable chaotic time series is proposed based on optimized extreme learning machine (ELM). In this algorithm, a presented composite chaos system and mutative scale chaos method are utilized first to search and optimize the parameters of ELM for improving the generalization performance. Then the optimized ELM is used to predict the multivariable chaotic time series of Rossler coupled system for single step and muti-step, and the scheme is compared with the congeneric method, which shows the validity and stronger ability against noise of the developed algorithm. Finally, the relation between prediction result and number of hidden neurons is discussed.

Keywords:

Authors and contacts

1.
Center for Control & Intelligence Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China;
2.
School of Iniformation Science & Technology Jiujiang University, Jiujiang 332005, China
Funds: Project supported by the National Natural Science Foundation of China (Grant No. 60874091), the Six Projects Sponsoring Talent Summits of Jiangsu Province, China (Grant No. SJ209006), the Research Fund for the Doctoral Program of Higher Education of China(Grant No. 20103223110003), the Natural Science Basic Research Project for Universities of Jiangsu Province, China (Grant No. 08KJD510022), the Natural Science Foundation of Jiangsu Province, China (Grant No. BK2010526), the Project for Introduced Talent in Nanjing University of Posts and Telecommunications, China (Grant No. NY209021), and the Scientific Research Innovation Program for the Graduat Students in Jiangsu Province, China (Grant No. CXZZ11 0400).

References

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[16]	Zhu Q Y, Qin A K, Suganthan P N, Huang G B 2005 Pattern Recognition 38 1759
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Cited By

[1]	Takens F 1981 In Lecture notes in mathematics, Vol.898 Dynamical systems and turbulence(Berlin:Springer)p366
[2]	Yan H, Wei P, Xiao X C 2009 Chin. Phys. B 18 3287
[3]	Samanta B 2011 Expert Syst. with Appl. 38 11406
[4]	Zhang C T, Ma Q L, Peng H 2010 Acta Phys. Sin. 59 7623 (in Chinese) [张春涛, 马千里, 彭宏 2010 59 7623]
[5]	Fang F,Wang H Y, Yang Z M 2011 Appl. Mech. Mater. 47 3180
[6]	Cao L Y, Mees A, Judd K 1998 Phys. D 121 75
[7]	Zhang Y, Guang W2009 Acta Phys. Sin. 58 0756 (in Chinese) [张勇, 关伟 2009 58 0756]
[8]	Lu S, Wang H Y 2006 Acta Phys.Sin. 55 572 (in Chinese) [卢山, 王海燕 2006 55 572]
[9]	Huang G B, Zhu Q Y, Siew C K 2006 Neuro Computing 70 489
[10]	Serre D 2002 Matrices:Theory and Appkications (New York: Springer) p145
[11]	Zhang T, Wang H W, Wang Z C 1999 Control and Decision 14 285 (in Chinese) [张彤, 王宏伟, 王子才 1999 控制与决策 14 285]
[12]	Sauer T, Yorke J A, Casdagli M 1991 J. Stat. Phys. 65 579
[13]	Wang H Y, Sheng Z H, Zhang J 2003 J. South. Univ. (Natural Science Edition) 33 115 (in Chinese) [王海燕, 盛昭瀚, 张进 2003 东南大学学报(自然科学版) 33 115]
[14]	Tong X J, Cui M G 2009 Science in China F 39 588 (in Chinese) [佟晓筠, 崔明根 2009 中国科学(F辑) 39 588]]
[15]	Pincus S 1995 Chaos 5 110
[16]	Zhu Q Y, Qin A K, Suganthan P N, Huang G B 2005 Pattern Recognition 38 1759
[17]	Bartlett P L 1998 IEEE Trans. Inform. Theory 44 525 040506-8

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Catalog

Vol. 61, Issue 4 - 2012-02-20

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