Robust global exponential synchronization of uncertain chaotic delayed neural networks via dual-stage impulsive control

This paper is concerned with the robust exponential synchronization problem of a class of chaotic delayed neural networks with different parametric uncertainties. A novel impulsive control scheme (so-called dual-stage impulsive control) is proposed. Based on the theory of impulsive functional differ...

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Veröffentlicht in:IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society. - 1996. - 40(2010), 3 vom: 15. Juni, Seite 831-44
1. Verfasser: Zhang, Huaguang (VerfasserIn)
Weitere Verfasser: Ma, Tiedong, Huang, Guang-Bin, Wang, Zhiliang
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2010
Zugriff auf das übergeordnete Werk:IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society
Schlagworte:Journal Article Research Support, Non-U.S. Gov't
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520 |a This paper is concerned with the robust exponential synchronization problem of a class of chaotic delayed neural networks with different parametric uncertainties. A novel impulsive control scheme (so-called dual-stage impulsive control) is proposed. Based on the theory of impulsive functional differential equations, a global exponential synchronization error bound together with some new sufficient conditions expressed in the form of linear matrix inequalities (LMIs) is derived in order to guarantee that the synchronization error dynamics can converge to a predetermined level. Furthermore, to estimate the stable region, a novel optimization control algorithm is established, which can deal with the minimum problem with two nonlinear terms coexisting in LMIs effectively. The idea and approach developed in this paper can provide a more practical framework for the synchronization of multiperturbation delayed chaotic systems. Simulation results finally demonstrate the effectiveness of the proposed method 
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650 4 |a Research Support, Non-U.S. Gov't 
700 1 |a Ma, Tiedong  |e verfasserin  |4 aut 
700 1 |a Huang, Guang-Bin  |e verfasserin  |4 aut 
700 1 |a Wang, Zhiliang  |e verfasserin  |4 aut 
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