The delay-dependent stability for the neutral type linear systems with delay is discussed.
讨论中立型线性时滞系统的时滞相关稳定性。
By using the criterion the stability robustness of discrete linear uncertain time-delay systems of neutral type is analysed.
利用所得判据分析了滞后中立型线性离散不确定性系统的鲁棒稳定性。
Some sufficient conditions are established for the oscillation of systems of parabolic differential equations of neutral type.
建立了一类中立型抛物微分方程组解的振动的若干充分条件。
In this paper, some sufficient conditions are obtained for the oscillation solutions of systems with high order partial differential equations of neutral type.
本文获得了一类中立型偏微分方程系统解振动的若干充分条件。
We investigate the existence of almost periodic solutions of functional differential equations of neutral type by Liapunov functional which is not positive definite.
利用李雅普·诺夫泛函研究中立型泛函微分方程的概周期解的存在性，其中李雅普·诺夫泛函不是正定的。
In this paper, the oscillation properties of solutions under a kind of boundary conditions for impulsive hyperbolic differential equation of neutral type are studied.
研究了一类边界条件下中立型脉冲双曲方程解的振动性，得到了每个非零解振动的若干充分条件。
This paper deals with the oscillation of the first order nonlinear neutral type functional differential equation, and obtains sufficient criterion of the equation oscillation.
本文研究一阶非线性中立型泛函微分方程的振动性。得到了该方程振动的充分性判别法则。
A sufficient condition guaranteeing the global asymptotical stability of the equilibrium point is derived for a class of neural network models with variable delay and neutral type delay.
摘要针对一类能够由中立型变延迟非线性微分方程描述的神经网络模型，给出了全局渐近稳定的不依赖于时间延迟的充分条件。
Research is done on the robust stability of multi -delay differential systems of neutral type subject to norm -bounded parameter uncertainty that played important roles in the control theory.
针对控制系统中应用十分广泛的不确定性问题进行了研究，主要研究了含范数有界参数不确定性中立型多延迟微分系统的鲁棒稳定性。
Compared with the existing stability results of neutral-type neural networks, the activation function of neural networks studied in this chapter has a more general form.
与现有中立型神经网络的稳定性结果相比，本章所研究的神经网络的激活函数具有更一般的形式。
Compared with the existing stability results of neutral-type neural networks, the activation function of neural networks studied in this chapter has a more general form.
与现有中立型神经网络的稳定性结果相比，本章所研究的神经网络的激活函数具有更一般的形式。
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