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Exponential synchronization of stochastic chaotic neural networks with mixed time delays and Markovian switching

机译：混合时滞和马尔可夫切换的随机混沌神经网络的指数同步

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摘要

This paper studies the exponential synchronization problem for a class of stochastic perturbed chaotic neural networks with both Markovian jump parameters and mixed time delays. The mixed delays consist of discrete and distributed time-varying delays. At first, based on a Halanay-type inequality for stochastic differential equations, by virtue of drive-response concept and time-delay feedback control techniques, a delay-dependent sufficient condition is proposed to guarantee the exponential synchronization of two identical Markovian jumping chaotic-delayed neural networks with stochastic perturbation. Then, by utilizing the Jensen integral inequality and a novel Lemma, another delay-dependent criterion is established to achieve the globally stochastic robust synchronization. With some parameters being fixed in advance, these conditions can be solved numerically by employing the Matlab software. Finally, a numerical example with their simulations is provided to illustrate the effectiveness of the presented synchronization scheme.
机译：本文研究了一类同时具有马尔可夫跳跃参数和混合时滞的随机扰动混沌神经网络的指数同步问题。混合延迟包括离散和分布式时变延迟。首先，基于随机微分方程的Halanay型不等式，利用驱动响应概念和时滞反馈控制技术，提出了一个时延相关的充分条件，以保证两个相同的马尔可夫跳跃混沌系统的指数同步。具有随机扰动的延迟神经网络。然后，通过利用詹森积分不等式和新颖的引理，建立了另一个依赖于延迟的准则，以实现全局随机鲁棒同步。通过预先确定一些参数，可以通过使用Matlab软件以数值方式解决这些条件。最后，提供了一个数值示例及其仿真，以说明所提出的同步方案的有效性。

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