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研究了带有时变时滞的惯性神经网络的同步问题。利用一个适当的变量变换将原始系统转换为一阶微分系统,构造了含有矩阵Kronecker积的Lyapunov-Krasovskii泛函(Lyapunov-Krasovskii functional, LKF),应用Jensen不等式、倒凸不等式和线性矩阵不等式(linear matrix inequality, LMI)技术来估计LKF的导数,得到了一个新的LMI形式的同步判据并基于同步判据给出了一个误差反馈控制器的设计方法。数值仿真例子验证了所得结果的有效性。
Abstract：This paper addresses the synchronization problem for inertial neural networks with time-varying delay. By using a proper variable substitution to transform the original system into a first-order differential system, constructing the Lyapunov-Krasovskii functional( LKF) containing the Kronecker product of matrices, applying Jensen inequality, the reciprocally convex inequality and the linear matrix inequality( LMI) technique to estimate the derivative of the LKF, a novel synchronization criterion in terms of LMIs is obtained, and a design method for the error feedback controller is presented based on the synchronization criterion. And a numerical simulation example shows the effectiveness of the proposed results.
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基本信息:
DOI：10.12194/j.ntu.20190402001
中图分类号:TP183
引用信息:
[1]陆双,高岩波.带有时变时滞的惯性神经网络的同步[J],2020,19(01):83-94.DOI:10.12194/j.ntu.20190402001.
基金信息:
国家自然科学基金项目(61273103,61573201,11772161)