Adaptive Control of Hyper-Chaotic Systems Based on Dynamic Structure RBF Neural Networks

Jing Wang; Hong Xia Gao; Zhen Yu Tan; Jin Feng Gao

doi:10.4028/www.scientific.net/AMM.229-231.2311

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 229-231Adaptive Control of Hyper-Chaotic Systems Based on...

Adaptive Control of Hyper-Chaotic Systems Based on Dynamic Structure RBF Neural Networks

Abstract:

An adaptive control scheme based on neural networks is presented for control of hyper-chaotic systems. Parameters of neural networks and controllers are adjusted automatically to ensure the stability of the closed-loop system. Numerical simulation illustrates that the proposed control scheme is valid for hyper-chaotic system.

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Periodical:

Applied Mechanics and Materials (Volumes 229-231)

Pages:

2311-2314

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https://doi.org/10.4028/www.scientific.net/AMM.229-231.2311

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Online since:

November 2012

Authors:

Jing Wang, Hong Xia Gao, Zhen Yu Tan, Jin Feng Gao

Keywords:

Adaptive Control, Dynamic Structure RBF Neural Networks, Hyper-Chaotic Systems

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References

[1] Lin T. C, Lee T. Y and Balas Valentina E: Chaos, Solitons&Fractals. Vol. 44 (2011), p.791.

Google Scholar

[2] Chen D. Y., Liu Y. X and Ma X. Y: Nonlinear Dynamics. Vol. 67 (2012), p.893.

Google Scholar

[3] Lin T. C, Lee T. Y: IEEE Transactions on Fuzzy Systems. Vol. 19 (2011) , p.623.

Google Scholar

[4] S. Fabri, V. Kadirkamanathan: IEEE Transactions on Neural Networks. Vol. 7 (1996), p.1151.

Google Scholar

[5] H. Sarimveis, A. Alexandridis, and G. Tsekouras et al: Industrial and Engineering Chemistry Research. Vol. 41 (2002), p.751.

Google Scholar

[6] A. Alexandridis, H. Sarimveis and B. George: Neural Networks. Vol 16 (2003), p.1003.

Google Scholar

[7] H. Ishibuchi, T. Murata and I.B. Türkşen: Fuzzy Sets and Systems. Vol. 89 (1997), p.135.

Google Scholar

[8] T. Matsumoto, L.O. Chua and K. Kobayashi et al: IEEE Transactions on Circuits and Systems. Vol. 33 (1986 ), p.1143.

Google Scholar