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Exponential adaptive synchronization of stochastic memristive chaotic recurrent neural networks with time-varying delays

机译:时变时滞随机忆阻混沌递归神经网络的指数自适应同步

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摘要

This paper is focused on the global exponential adaptive synchronization problem of two stochastic memristive chaotic neural networks with both stochastic disturbance and time-varying delays. First, in order to develop the guaranteed cost control, a periodically alternate adaptive rule is designed. Then, by constructing appropriate Lyapunov-Krasovskii functionals, several easily verified synchronization criteria are derived to guarantee exponential adaptive synchronization of drive-response stochastic memristive chaotic recurrent neural networks. Lastly, a numerical simulation is carried out to demonstrate the effectiveness of the proposed results. (C) 2017 Elsevier B.V. All rights reserved.
机译:本文主要研究两个具有随机扰动和时变时滞的随机忆阻混沌神经网络的全局指数自适应同步问题。首先,为了发展有保证的成本控制,设计了一种周期性交替的自适应规则。然后,通过构造适当的Lyapunov-Krasovskii泛函,导出几个容易验证的同步准则,以保证驱动响应随机忆阻性混沌递归神经网络的指数自适应同步。最后,进行了数值模拟以证明所提出结果的有效性。 (C)2017 Elsevier B.V.保留所有权利。

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