Robust Global Exponential Synchronization of Uncertain Chaotic Delayed Neural Networks via Dual-Stage Impulsive Control
Robust Global Exponential Synchronization of Uncertain Chaotic Delayed Neural Networks via Dual-Stage Impulsive Control
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DOI：
10.1109/tsmcb.2009.2030506
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发表时间：
2010-06
期刊：
影响因子：
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通讯作者：
Huaguang Zhang;Tiedong Ma;G. Huang;Zhiliang Wang
中科院分区：
文献类型：
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作者：
Huaguang Zhang;Tiedong Ma;G. Huang;Zhiliang Wang
研究了一类具有不同参数不确定性的混沌时滞神经网络的鲁棒指数同步问题。提出了一种新的脉冲控制方案（所谓的双级脉冲控制）。基于脉冲泛函微分方程理论，导出了全局指数同步误差的有界性，并给出了以线性矩阵不等式（LMI）形式表示的新的充分条件，以保证同步误差动力学收敛到预定的水平.此外，为了估计稳定区域，建立了一种新的优化控制算法，该算法可以有效地处理线性矩阵不等式中两个非线性项共存的最小值问题。本文提出的思想和方法为多扰动时滞混沌系统的同步提供了一个更实用的框架。仿真结果验证了该方法的有效性。
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.