Kumar R Suresh, Sugumaran G, Raja R, Zhu Quanxin, Raja U Karthik
Department of Electrical and Electronic Engineering, Anna University Regional Centre, Coimbatore, 641 047 India.
Department of Electrical and Electronic Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, 641 008 India.
Cogn Neurodyn. 2016 Feb;10(1):85-98. doi: 10.1007/s11571-015-9356-y. Epub 2015 Sep 29.
This paper analyzes the global asymptotic stability of a class of neural networks with time delay in the leakage term and time-varying delays under impulsive perturbations. Here the time-varying delays are assumed to be piecewise. In this method, the interval of the variation is divided into two subintervals by its central point. By developing a new Lyapunov-Krasovskii functional and checking its variation in between the two subintervals, respectively, and then we present some sufficient conditions to guarantee the global asymptotic stability of the equilibrium point for the considered neural network. The proposed results which do not require the boundedness, differentiability and monotonicity of the activation functions, can be easily verified via the linear matrix inequality (LMI) control toolbox in MATLAB. Finally, a numerical example and its simulation are given to show the conditions obtained are new and less conservative than some existing ones in the literature.
本文分析了一类在泄漏项中具有时滞且在脉冲扰动下具有时变时滞的神经网络的全局渐近稳定性。这里假设时变时滞是分段的。在该方法中,变化区间通过其中心点被划分为两个子区间。通过构造一个新的Lyapunov-Krasovskii泛函,并分别检验其在两个子区间之间的变化,进而给出了一些充分条件以保证所考虑神经网络平衡点的全局渐近稳定性。所提出的结果不需要激活函数有界、可微和单调,可通过MATLAB中的线性矩阵不等式(LMI)控制工具箱轻松验证。最后,给出了一个数值例子及其仿真,以表明所得到的条件是新的,且比文献中一些现有条件保守性更低。