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首页> 外文期刊>Neural processing letters >Robust H_∞ Filtering of Stochastic Switched Complex Dynamical Networks with Parameter Uncertainties, Disturbances, and Time-Varying Delays
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Robust H_∞ Filtering of Stochastic Switched Complex Dynamical Networks with Parameter Uncertainties, Disturbances, and Time-Varying Delays

机译:具有参数不确定性,扰动和时变时滞的随机切换复杂动态网络的鲁棒H_∞滤波

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摘要

This paper investigates the problem of stability analysis for switched complex dynamical networks with mixed time- varying delays and parameter uncertainties. The switched complex dynamical networks are composed ofmmodes that are switched from one to another based on time, state, etc. Although, the active subsystem is known in any instance, but the switching law such as transition probabilities are not known. The model for each mode is considered affine with matched and unmatched perturbations. The main purpose of the addressed problem is to design a filter error for the switched complex dynamical networks such that the dynamics of the error converges to the asymptotically irrespective of the admissible parameter variations with the gains. Then, by utilizing the Lyapunov functional method, the stochastic analysis combined with the matrix inequality techniques, a sufficient condition in terms of linear matrix inequalities is presented to ensure the H8 performance of the complex dynamical system models. Finally, a numerical example is presented to illustrate the effectiveness of the proposed design method.
机译:本文研究了具有混合时变时滞和参数不确定性的切换复杂动力网络的稳定性分析问题。交换的复杂动态网络由基于时间,状态等从一种模式切换到另一种模式的m模式组成。尽管在任何情况下主动子系统都是已知的,但是诸如转换概率之类的转换定律是未知的。每种模式的模型都被视为仿射,具有匹配和不匹配的扰动。解决的问题的主要目的是为切换的复杂动力学网络设计一个滤波器误差,以使误差的动力学收敛于渐近线,而与增益的容许参数变化无关。然后,利用Lyapunov泛函方法,结合矩阵不等式技术进行了随机分析,提出了线性矩阵不等式的充分条件,以确保复杂动力系统模型的H8性能。最后,给出了一个数值例子来说明所提出的设计方法的有效性。

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