College of Science, China Three Gorges University, Yichang, Hubei, 443002, China.
ISA Trans. 2018 Mar;74:88-98. doi: 10.1016/j.isatra.2018.02.002. Epub 2018 Feb 16.
This paper focuses on delay-dependent passivity analysis for a class of memristive impulsive inertial neural networks with time-varying delays. By choosing proper variable transformation, the memristive inertial neural networks can be rewritten as first-order differential equations. The memristive model presented here is regarded as a switching system rather than employing the theory of differential inclusion and set-value map. Based on matrix inequality and Lyapunov-Krasovskii functional method, several delay-dependent passivity conditions are obtained to ascertain the passivity of the addressed networks. In addition, the results obtained here contain those on the passivity for the addressed networks without impulse effects as special cases and can also be generalized to other neural networks with more complex pulse interference. Finally, one numerical example is presented to show the validity of the obtained results.
本文针对一类时变时滞的忆阻脉冲惯性神经网络，研究了时滞相关的被动性分析问题。通过选择合适的变量变换，将忆阻惯性神经网络重写为一阶微分方程。本文所提出的忆阻模型被视为一个切换系统，而不是采用微分包含和集值映射理论。基于矩阵不等式和 Lyapunov-Krasovskii 泛函方法，得到了几个时滞相关的被动性条件，以确定所研究网络的被动性。此外，所得结果包含了在没有脉冲效应的情况下所研究网络的被动性的特殊情况，并且可以推广到具有更复杂脉冲干扰的其他神经网络。最后，通过一个数值实例验证了所得结果的有效性。
