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首页> 外文期刊>International Journal of Physical Sciences >Anti-synchronization of chaotic neural networks with time-varying delays via linear matrix inequality (LMI)
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Anti-synchronization of chaotic neural networks with time-varying delays via linear matrix inequality (LMI)

机译:线性矩阵不等式(LMI)的时变时滞混沌神经网络的反同步

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摘要

In this paper, anti-synchronization problem of two identical chaotic neural networks with time-varying delays is proposed. By using time-delay feedback control technique, mean value theorem and the Leibniz-Newton formula, and by constructing appropriately Lyapunov-Krasovskii functional, sufficient condition is proposed to guarantee the asymptotically anti-synchronization of two identical chaotic neural networks. This condition, which is expressed in terms of linear matrix inequality, rely on the connection matrix in the drive and response networks as well as the suitable designed feedback gains in the response network. Finally, the anti-synchronization of two chaotic cellular neural network and Hopfield neural network with time-varying delays are considered to illustrate the effectiveness of the proposed control scheme, in which, when compared with the nonlinear feedback control method, the proposed method shows superior performance.
机译:本文提出了两个具有时变时滞的相同混沌神经网络的反同步问题。通过使用时滞反馈控制技术,均值定理和Leibniz-Newton公式,并通过适当构造Lyapunov-Krasovskii泛函,提出了充分的条件来保证两个相同混沌神经网络的渐近反同步。用线性矩阵不等式表示的这种条件依赖于驱动和响应网络中的连接矩阵以及响应网络中合适的设计反馈增益。最后,考虑了具有时变时滞的两个混沌细胞神经网络和Hopfield神经网络的反同步,以说明所提出的控制方案的有效性。与非线性反馈控制方法相比,该方法具有更好的控制效果。性能。

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