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A hybrid strategy to control uncertain nonlinear chaotic system |
| Yongbo Sui(隋永波)1, Yigang He(何怡刚)1, Wenxin Yu(于文新)2, Yan Li(李燕)3 |
1. The School of Electrical and Automation Engineering, Hefei University of Technology, Hefei 230009, China;
2. School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan 411105, China;
3. College of Electrical and Information Engineering, Hunan University, Changsha 430100, China |
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Abstract In this paper, a new method, based on firefly algorithm (FA) and extreme learning machine (ELM), is proposed to control chaos in nonlinear system. ELM is an efficient predicted and classified tool, and can match and fit nonlinear systems efficiently. Hence, mathematical model of uncertain nonlinear system is obtained indirectly. For higher fitting accuracy, a novel swarm intelligence algorithm FA is drawn in our proposed way. The main advantage is that our proposed method can remove the limitation that mathematical model must be known clearly and can be applied to unknown nonlinear chaotic system.
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Received: 26 March 2017
Revised: 19 July 2017
Accepted manuscript online:
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PACS:
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05.45.-a
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(Nonlinear dynamics and chaos)
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05.45.Gg
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(Control of chaos, applications of chaos)
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| Fund: Project supported by the National Natural Science Foundation of China (Grant No. 51577046), the State Key Program of the National Natural Science Foundation of China (Grant No. 51637004), and the National Key Research and Development Plan "Important Scientific Instruments and Equipment Development" (Grant No. 2016YFF0102200). |
Corresponding Authors:
Yigang He
E-mail: 18655136887@163.com
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Cite this article:
Yongbo Sui(隋永波), Yigang He(何怡刚), Wenxin Yu(于文新), Yan Li(李燕) A hybrid strategy to control uncertain nonlinear chaotic system 2017 Chin. Phys. B 26 100503
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| [1] |
Lorenz E N 1963 J. Atmos Sci. 20 130
|
| [2] |
Li C L and Zhang J 2016 Int. J. Syst. Sci. 47 2440
|
| [3] |
Yin J L, Zhao L W and Tian L X 2014 Chin. Phys. B 23 020204
|
| [4] |
Chen H H, Sheu G J and Lin Y L 2009 Nonlinear Anal. Theor. 70 4393
|
| [5] |
Hu J, Qiu Y and Lu H 2016 Appl. Math. Model. 40 8265
|
| [6] |
Li C 2012 Commun. Nonlinear Sci. 17 405
|
| [7] |
Liu S and Chen L Q 2013 Chin. Phys. B 22 100506
|
| [8] |
Li C, Su K and Wu L 2013 J. Comput. Nonlinear Dyn. 8 031005
|
| [9] |
Liu F, Sun C X, Si-Ma W X, Liao R J and Guo F 2006 Phys. Lett. A 357 218
|
| [10] |
Hsu C F, Lin C M and Yeh R G 2013 Appl. Soft Comput. 4 1620
|
| [11] |
Li M B, Huang G B, Saratchandran P and Sundararajan N 2005 Neurocomputing 68 306
|
| [12] |
Zhang Y, Zhang L and Li P 2016 Neurocomputing 174 286
|
| [13] |
Alexandros I, Anastasios T and Ioannis P 2015 Procedia Comput. Sci. 51 2814
|
| [14] |
Sharma S, Malik H and Khatri A 2015 Procedia Comput. Sci. 70 814
|
| [15] |
Luo M, Li C S, Zhang X Y, Li R H and An X 2016 ISA Trans. 65 556
|
| [16] |
Francisco A B, Thomas W R and Flávio M V 2017 Neurocomputing 239 238
|
| [17] |
Yu W X, Sui Y B and Wang J N 2016 J. Electron. Test. 32 459
|
| [18] |
Yuan X F, Zhao J J, Yang Y M and Wang Y N 2014 Appl. Soft Comput. 17 12
|
| [19] |
Lei Y M and Zhang H X 2017 Chin. Phys. B 26 030502
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