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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.
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Keywords:
- extreme learning machine /
- multivariable chaotic time series /
- prediction of chaotic time series /
- composite chaos optimization
[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 物理学报 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 物理学报 58 0756]
[8] Lu S, Wang H Y 2006 Acta Phys.Sin. 55 572 (in Chinese) [卢山, 王海燕 2006 物理学报 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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[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 物理学报 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 物理学报 58 0756]
[8] Lu S, Wang H Y 2006 Acta Phys.Sin. 55 572 (in Chinese) [卢山, 王海燕 2006 物理学报 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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