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A delay-dependent approach to robust control for neutral uncertain neural networks with mixed interval time-varying delays

机译:具有混合区间时变时滞的不确定神经网络的时滞依赖鲁棒控制方法

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摘要

This paper considers the problem of delay-dependent global robust stabilization for discrete, distributed and neutral interval time-varying delayed neural networks described by nonlinear delay differential equations of the neutral type. The parameter uncertainties are norm bounded. The activation functions are assumed to be bounded and globally Lipschitz continuous. Using a Lyapunov functional approach and linear matrix inequality (LMI) techniques, the stability criteria for the uncertain neutral neural networks with interval time-varying delays are established in the form of LMIs, which can be readily verified using the standard numerical software. An important feature of the result reported is that all the stability conditions are dependent on the upper and lower bounds of the delays. Another feature of the results lies in that it involves fewer free weighting matrix strategy, and upper bounds of the inner product between two vectors are not introduced to reduce the conservatism of the criteria. Two illustrative examples are provided to demonstrate the effectiveness and the reduced conservatism of the proposed method.
机译:本文考虑了由中立型非线性时滞微分方程描述的离散,分布和中立区间时变时滞神经网络的时滞相关全局鲁棒镇定问题。参数不确定性是有界的。假设激活函数是有界的,并且全局Lipschitz是连续的。使用Lyapunov函数方法和线性矩阵不等式(LMI)技术,以LMI的形式建立了不确定的具有间隔时变延迟的中性神经网络的稳定性标准,可以使用标准数值软件轻松地对其进行验证。报告结果的一个重要特征是,所有稳定性条件都取决于延迟的上限和下限。结果的另一个特征在于,它涉及较少的自由加权矩阵策略,并且没有引入两个向量之间的内积上限以减小准则的保守性。提供了两个说明性示例,以证明所提出方法的有效性和降低的保守性。

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