研究了离散时间神经网络的全局指数稳定性问题。
In this paper, the global exponential stability of discrete-time neural networks is discussed.
并且由此定理获得该类网络全局指数稳定的几个判据。
Some new criteria on globally exponential stability of the networks are obtained.
讨论了一类具有有界可变时滞分离变量系统平衡点的全局指数稳定性。
The global exponential stability of equilibrium states of a class of nonlinear separated variables systems with bounded time-varying delays was investigated.
讨论了一类具有有界可变时滞分离变量系统平衡点的全局指数稳定性。
The stabilization problem for a class of nonlinear continuous control systems with separated variables is investigated.
基于拉格朗日常数变易法，给出这类系统全局指数稳定的一个充分条件。
Based on the Lagrange formula with constants variables, some new sufficient conditions for ensuring globally exponential stability of such systems are derived.
第三章研究了一类具有变时滞的模糊bam神经网络模型的全局指数稳定性。
Chapter 3 introduces global exponential stability of a class of fuzzy BAM neural networks with variable delays.
在分析无条件全局指数稳定性时，我们将时滞微分不等式引入到稳定性的研究中。
Global exponential stability theorems are given by using a method based on delay differential inequality.
针对模糊时滞系统，设计了非线性状态反馈控制器以确保闭环系统全局指数稳定。
Global exponential stability of fuzzy control systems with delays is studied, a nonlinear state feedback controller is designed to ensure the global exponential stability of the closed-loop system.
本文目的是给出判别几类神经网络存在唯一全局指数稳定平衡点的实用有效的判据。
In this thesis, the feasible criteria of several ANNs to criticize existence of the unique global exponential stable equilibrium.
主要考察了一类动态系统——带有不同时间尺度竞争神经网络的全局指数稳定性问题。
This paper studies the problem of globally exponential stability for the cellular neural networks.
摘要研究了具反应扩散有限连续分布细胞神经网络的平衡点的存在性及全局指数稳定性问题。
The existence of the equilibrium point and global exponential stability of distributed delays neural networks with reaction-diffusion terms are investigated in this paper.
第三章对变系数离散时间混合时滞细胞神经网络模型周期解的存在性与全局指数稳定性进行了讨论。
In Chapter 3, we discuss the existence and global exponential stability of periodic solutions for discrete-time cellular neural network with mixed delays and variable coefficients.
利用M矩阵理论，通过构造适当的向量李雅普诺夫函数，研究一类具有时变时间滞后的线性关联大系统的全局指数稳定性。
The global exponential stability of a class of linear interconnected large scale systems with time delays was analyzed based on M matrix theory and by constructing a vector Lyapunov function.
运用比较原理和导数不连续的李雅普诺夫函数，结合分解集结等方法，研究具有滞后的测度型线性时变脉冲扰动大系统的全局指数稳定性。
The stability of time-delay and time-varying large scale systems with impulsive effect is investigated by means of the comparison principle and vector Lyapunov function with discontinuous derivative.
讨论了混合时滞区间神经网络的全局鲁棒指数稳定性。
The global robust exponential stability is investigated for interval neural networks with mixed delay.
它们具有全局渐近稳定性，且通过配置估值器的极点可按指数衰减速率使初始状态估值的影响快速消失。
They have globally asymptotic stability, and can forget the effect of the initial state estimates at an exponentially decaying rate by assigning the poles of the estimators.
它们具有全局渐近稳定性，且通过配置估值器的极点可按指数衰减速率使初始状态估值的影响快速消失。
They have globally asymptotic stability, and can forget the effect of the initial state estimates at an exponentially decaying rate by assigning the poles of the estimators.
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