Parameter estimation for chaotic systems based on hybrid biogeography-based optimization

Lin Jian; Xu Li

doi:10.7498/aps.62.030505

Abstract

Parameter estimation for chaotic system is, in fact, a multi-dimensional optimization problem. By combining biogeography-based optimization (BBO) with harmony search (HS) and opposition-based learning (OBL), a hybrid BBO scheme is proposed for solving the chaotic parameter estimation problem. The HS is used to enhance the local search ability of BBO, and OBL is employed to increase the diversity of the initial population, thereby improving the optimizing performance. The effectiveness and robustness of the proposed scheme are verified by numerical simulations on two typical chaotic systems.

Keywords:

Authors and contacts

Lin Jian^{1 2},
Xu Li¹

1.
College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China;
2.
College of Printing Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
Funds: Project supported by the Special Funds of the National Natural Science Foundation of China (Grant No. 61142005).

References

[1]	Maybhate A, Amritkar R E 1999 Phys. Rev. E 59 284
[2]	Saha P, Banerjee S, Chowdhury A R 2004 Phys. Lett. A 326 133
[3]	Xu D L, Lu F F 2005 Chaos Soliton. Fract. 25 361
[4]	Fotsina H B, Woafob P 2005 Chaos Soliton. Fract. 24 1363
[5]	Peng H P, Li L X, Yang Y X, Zhang X H, Gao Y 2007 Acta Phys. Sin. 56 6246 (in Chinese) [彭海朋, 李丽香, 杨义先, 张小红, 高洋 2007 物理学报 56 6246]
[6]	Wang K, Pei W J, Zhang Y F, Zhou S Y, Shao S 2011 Acta Phys. Sin. 60 070502 (in Chinese) [王开, 裴文江, 张毅峰, 周思源, 邵硕 2011 物理学报 60 070502]
[7]	Dai D, Ma X K, Li F C, You Y 2002 Acta Phys. Sin. 51 2459 (in Chinese) [戴栋, 马西奎, 李富才, 尤勇 2002 物理学报 51 2459]
[8]	He Q, Wang L, Liu B 2007 Chaos Soliton. Fract. 34 654
[9]	Wang J Y, Huang D X 2008 Acta Phys. Sin. 57 2755 (in Chinese) [王钧炎, 黄德先 2008 物理学报 57 2755]
[10]	Ho W H, Chou J H, Guo C Y 2010 Nonlinear Dyn. 61 29
[11]	Simon D 2008 IEEE Trans. Evolut. Comput. 12 702
[12]	Simon D 2011 Evol. Comput. 19 167
[13]	Tizhoosh H R 2005 International Conference on Computational Intelligence for Modelling Control and Automation Vienna, Austria, November 28-30, 2005 p695
[14]	Wang H, Wu Z, Rahnamayan S, Liu Y, Ventresca M 2011 Inform. Sci. 181 4699
[15]	Rahnamayan S, Tizhoosh H R, Salama M M A 2008 IEEE Trans. Evolut. Comput. 12 64
[16]	Geem Z W, Kim J H 2001 Simulation 76 60
[17]	Khorram E, Jaberipour M 2011 Energ. Convers. Manage 52 1550

Cited By

[1]	Maybhate A, Amritkar R E 1999 Phys. Rev. E 59 284
[2]	Saha P, Banerjee S, Chowdhury A R 2004 Phys. Lett. A 326 133
[3]	Xu D L, Lu F F 2005 Chaos Soliton. Fract. 25 361
[4]	Fotsina H B, Woafob P 2005 Chaos Soliton. Fract. 24 1363
[5]	Peng H P, Li L X, Yang Y X, Zhang X H, Gao Y 2007 Acta Phys. Sin. 56 6246 (in Chinese) [彭海朋, 李丽香, 杨义先, 张小红, 高洋 2007 物理学报 56 6246]
[6]	Wang K, Pei W J, Zhang Y F, Zhou S Y, Shao S 2011 Acta Phys. Sin. 60 070502 (in Chinese) [王开, 裴文江, 张毅峰, 周思源, 邵硕 2011 物理学报 60 070502]
[7]	Dai D, Ma X K, Li F C, You Y 2002 Acta Phys. Sin. 51 2459 (in Chinese) [戴栋, 马西奎, 李富才, 尤勇 2002 物理学报 51 2459]
[8]	He Q, Wang L, Liu B 2007 Chaos Soliton. Fract. 34 654
[9]	Wang J Y, Huang D X 2008 Acta Phys. Sin. 57 2755 (in Chinese) [王钧炎, 黄德先 2008 物理学报 57 2755]
[10]	Ho W H, Chou J H, Guo C Y 2010 Nonlinear Dyn. 61 29
[11]	Simon D 2008 IEEE Trans. Evolut. Comput. 12 702
[12]	Simon D 2011 Evol. Comput. 19 167
[13]	Tizhoosh H R 2005 International Conference on Computational Intelligence for Modelling Control and Automation Vienna, Austria, November 28-30, 2005 p695
[14]	Wang H, Wu Z, Rahnamayan S, Liu Y, Ventresca M 2011 Inform. Sci. 181 4699
[15]	Rahnamayan S, Tizhoosh H R, Salama M M A 2008 IEEE Trans. Evolut. Comput. 12 64
[16]	Geem Z W, Kim J H 2001 Simulation 76 60
[17]	Khorram E, Jaberipour M 2011 Energ. Convers. Manage 52 1550

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