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The concept of controlled λ-exponentially contractive set for the systems is introduced,and relation between the set and that for its Euler approximation discrete-time system is also established.

首先,引入连续时间系统的受控λ-收缩集概念,并建立了与其Euler逼近的离散系统的受控收缩集之间的关系。

In this paper, a new variable structure control method is presented.

本文针对离散时间系统,提出一种新的变结构控制方法。

In this thesis, variable structure control in discrete systems is mainly studied.

本文主要针对离散情况下的变结构控制进行了研究。

The variable structure control of discrete time systems based on intelligent control is presented in this paper.

提出了一种基于智能控制的离散变结构系统。

Then characteristics and design methods of variable structure control for discrete-time systems are introduced.

然后讨论了离散系统的变结构控制的设计特点和方法。

Variable structure control of discrete-time systems and its applications in shot-concrete robot are discussed in this paper.

本文探讨了离散系统的变结构控制理论及其在喷浆机器人中的应用。

The problem of the design of variable structure control for discrete-time systems is considered.

针对离散时间系统变结构控制的设计问题进行了研究。

New methods of variable structure control for discrete time system.

离散时间系统的变结构控制新方法。

With an energy method,the optimal order convergence rate in discrete H1 norm is proved and the stability in H1 norm is proved without any restrictive conditions about steps Δt and h.

利用变分形式及能量方法,得到离散H1范数的最优阶先验误差估计及稳定性。

In accordance with discrete, non-symmetry and time-delay feedback neural networks, we provide some theoretical criteria to judge the stability for designing the networks. On continuous feedback networks, we built an algorithm to estimate the convergence region. On the HNN feedback networks, an effective optimization algorithm based on the annealing strategy and the chaotic neural network is presented, and some disciplinary conclusions are provided to guide the selection of parameters. Simulation results on benchmark problems demonstrate that the performances of our algorithm are distinctly superior to those of the classic HNN based on gradient descend and stochastic optimization methods based on probabilistic distribution.

针对离散、非对称和时延等三种类型的动态反馈网络,给出了可用于网络设计的稳定性判别准则;针对连续动态反馈网络,建立了其收敛域估计的一个算法;针对以HNN模型描述的动态反馈网络,基于退火策略和混沌神经网络提出了一种高效的优化算法,建立了可用于指导参数选择的一些规律性结论,对典型算例的仿真研究显示,改进算法在各类性能上相对基于梯度下降的传统HNN模型和基于概率分布的随机优化算法,具有明显的优越性。

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