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Linear matrix inequality approach to exponential synchronization of a class of chaotic neural networks with time-varying delays

机译:一类具有时变时滞的混沌神经网络指数同步的线性矩阵不等式方法

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摘要

In this paper, a synchronization scheme for a class of chaotic neural networks with time-varying delays is presented.This class of chaotic neural networks covers several well-known neural network, such a Hopfield neural networks, cellular neural networks, and bidirectional associative memory networks. The obtained criteria are expressed in terms of linear matrix inequalities, thus they can be efficiently verified. A comparison between our results and the previous results shows that our results are less restrictive.
机译:本文提出了一种具有时变时滞的混沌神经网络的同步方案,该类混沌神经网络涵盖了几种著名的神经网络,如Hopfield神经网络,细胞神经网络和双向联想记忆。网络。所获得的标准以线性矩阵不等式表示,因此可以对其进行有效验证。我们的结果与以前的结果之间的比较表明,我们的结果限制较少。

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