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Robust Adaptive Exponential Synchronization of Stochastic Perturbed Chaotic Delayed Neural Networks with Parametric Uncertainties

机译:具有参数不确定性的随机扰动混沌时滞神经网络的鲁棒自适应指数同步

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This paper investigates the robust adaptive exponential synchronization in mean square of stochastic perturbed chaotic delayed neural networks with nonidentical parametric uncertainties. A robust adaptive feedback controller is proposed based on Gronwally’s inequality, drive-response concept, and adaptive feedback control technique with the update laws of nonidentical parametric uncertainties as well as linear matrix inequality (LMI) approach. The sufficient conditions for robust adaptive exponential synchronization in mean square of uncoupled uncertain stochastic chaotic delayed neural networks are derived in terms of linear matrix inequalities (LMIs). The effect of nonidentical uncertain parameter uncertainties is suppressed by the designed robust adaptive feedback controller rapidly. A numerical example is provided to validate the effectiveness of the proposed method.
机译:本文研究具有不确定参量不确定性的随机扰动混沌时滞神经网络均方的鲁棒自适应指数同步。提出了一种鲁棒的自适应反馈控制器,该控制器基于Gronwally的不等式,驱动器响应的概念以及自适应反馈控制技术,该技术具有不相同的参数不确定性的更新定律以及线性矩阵不等式(LMI)方法。根据线性矩阵不等式(LMI),推导了未耦合不确定随机混沌延迟神经网络均方中鲁棒自适应指数同步的充分条件。所设计的鲁棒自适应反馈控制器迅速抑制了不确定参量不确定性的影响。数值例子验证了所提方法的有效性。

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