一类具有时变时滞的神经网络的稳定性分析

研究了一类具有时变时滞的神经网络的稳定性,首先将要研究的模型转化为数学系统模型,利用Lyapunov-Krasovskii稳定性理论和线性矩阵不等式技术(LMI方法),精炼和推广了一些已有的结果,所得结果为时变时滞神经网络提供了新的稳定性判定准则.     

具有非对称通信时滞和切换拓扑的高阶多智能体系统的一致性

研究具有切换有向拓扑和非对称时变时滞的高阶多智能体系统的一致性问题.通过引入正交线性变换和Lyapunov-Krasovskii泛函方法,依据线性矩阵不等式给出了系统解决一致性问题的充分条件以及可容许时变时滞的上界估计.其主要贡献是基于Lyapunov方程和代数不等式建立了协议参数的显性设计,该参数设计形式简单且易于计算,并保证了所给充分条件中线性矩阵不等式的可解性,使得高阶多智能体系统的一致性在切换有向拓扑下对非对称时变时滞是鲁棒的.     

时滞广义时变系统的容许性分析与镇定

研究了时滞广义时变系统的容许性与镇定性问题.首先,基于广义Lyapunov不等式、线性矩阵不等式和受限等价方法,建立时滞广义时变系统的Lyapunov不等式,将时滞广义时变系统的容许性问题转化为求解时滞广义时变系统的Lyapunov不等式问题,得到了系统容许的充分条件.然后,根据充分条件进一步研究了时滞广义时变系统的镇定问题,给出了状态反馈镇定器的设计方法.最后,通过数值算例验证了所得结论的有效性.     

具有随机切换非线性的时滞神经网络的非脆弱状态估计

探讨了带有随机切换非线性和混合时滞的不确定神经网络的非脆弱状态估计问题.首先,给出了所研究神经网络的数学模型;其次,基于有效信息设计非脆弱状态估计器,采用范数有界不确定性刻画了状态估计器增益矩阵的摄动现象;再次,基于Lyapunov稳定性定理给出了新的线性矩阵不等式描述的稳定性条件;最后,给出了仿真实验验证所提出的状态估计算法的可行性.     

一类含时变分布时滞的退化中立型系统的鲁棒稳定性(英文)

考虑含分布时滞的退化中立型系统的鲁棒稳定性.利用算子Ω的稳定性和线性矩阵不等式得到一个新的鲁棒稳定性判据,本判据将中立型时滞、时变离散时滞、时变分布时滞和退化中立型系统一起考虑,相比已有文献具有较低的保守性.利用Matlab可以验证本判据的有效性.     

具有两个加性输入时滞系统的状态反馈控制

主要考虑了具有两个加性输入时滞的网络化控制系统的状态反馈控制问题.首先考虑具有两个加性时滞的系统稳定性,构造适当的Lyapunov泛函,不需要所有的矩阵正定,只需其整体正定.其次在估计Lyapunov泛函沿系统的导数时,针对时滞范围的不同,引入了不同的松弛矩阵,得出系统保守性较小的稳定条件.在此基础上,研究了系统状态反馈控制,最后得出闭环系统渐近稳定的充分条件,并给出控制器的设计方法.     

一类不确定区间时变时滞系统的时滞相关鲁棒H_∞控制

研究了一类不确定区间时变状态时滞系统的鲁棒H_∞控制问题.基于Lyapunov稳定性理论和线性矩阵不等式,采用自由权矩阵方法,得到使得相应闭环系统渐近稳定且具有H_∞性能的时滞相关充分条件,并给出状态反馈鲁棒H_∞控制律的设计方法.仿真实例表明了该方法的有效性.     

具有离散时滞和分布时滞的中立型时滞系统的鲁棒稳定性

本文研究了具有离散时滞和分布时滞的不确定中立型系统的鲁棒稳定性的问题,考查了可能随时间变化的具有范数有界的不确定性.在对Lyapunov-Krasovskii泛函求导数时,不进行放大估计,而通过引入恰当的零项,构造线性矩阵不等式,从而获得基于LMI的时滞相关稳定的新判据.最后的数值例子说明了所得的结论有效性.     

带有时变时滞的中立型神经网络的鲁棒指数稳定性

研究一类带有时变时滞的中立型神经网络的全局指数稳定性问题.通过构造LyapunovKrasovskii泛函并使用线性矩阵不等式方法,建立了保障时滞神经网络全局指数稳定的新的时滞相关充分条件.这些条件用线性矩阵不等式表达.进一步,文章对一类不确定时滞中立型神经网络给出了鲁棒全局指数稳定的新判据.     

具有leakage项时滞与脉冲影响的模糊细胞神经网络指数同步

通过构造恰当的Lyapunov泛函并利用一些不等式分析技巧,得到了基于p-范数的耦合神经网络系统全局指数同步条件.所研究模型同时包含leakage项时滞、时变时滞和有界分布时滞,所得结果包含了之前一些文献的结论,适用范围更广泛.     

Exponential state estimator design for discrete‐time neural networks with discrete and distributed time‐varying delays

In this article, the probl of state estimation for discrete‐time neural networks with mixed time‐varying delays is investigated. The mixed time delays consist of both discrete and distributed delays. An appropriate Lyapunov–Krasovskii functional put forward to reflect the mixed time‐varying delays is proposed to establish sufficient conditions for the existence of admissible state estimators. The conditions are described in the form of linear matrix inequalities (LMIs), which guarantee the estimation error to be globally exponentially stable in the presence of mixed time‐varying delays. Then, the desired estimator matrix gain can be characterized in terms of the solution to these LMIs. A numerical example is addressed to show the effectiveness of the proposed design method. © 2014 Wiley Periodicals, Inc. Complexity 20: 38–48, 2014     

Delay-dependent robust exponential state estimation of Markovian jumping fuzzy Hopfield neural networks with mixed random time-varying delays

This paper investigates delay-dependent robust exponential state estimation of Markovian jumping fuzzy neural networks with mixed random time-varying delay. In this paper, the Takagi–Sugeno (T–S) fuzzy model representation is extended to the robust exponential state estimation of Markovian jumping Hopfield neural networks with mixed random time-varying delays. Moreover probabilistic delay satisfies a certain probability-distribution. By introducing a stochastic variable with a Bernoulli distribution, the neural networks with random time delays is transformed into one with deterministic delays and stochastic parameters. The main purpose is to estimate the neuron states, through available output measurements such that for all admissible time delays, the dynamics of the estimation error is globally exponentially stable in the mean square. Based on the Lyapunov–Krasovskii functional and stochastic analysis approach, several delay-dependent robust state estimators for such T–S fuzzy Markovian jumping Hopfield neural networks can be achieved by solving a linear matrix inequality (LMI), which can be easily facilitated by using some standard numerical packages. The unknown gain matrix is determined by solving a delay-dependent LMI. Finally some numerical examples are provided to demonstrate the effectiveness of the proposed method.     

State Estimation and Reliable Control of Singular Markovian Systems with Distributed State Delays and Input Delays

This paper is concerned with state estimation and reliable control for singular Markovian jump systems with distributed state delays and input delays. Firstly, an observer is designed to estimate the system states, and a reliable controller is proposed based on the state estimates. Moreover, some conditions for the mean-square exponential admissibility of the overall closed-loop system are derived in terms of strict linear matrix inequality (LMI).     

Stabilization of switched linear systems with mode-dependent time-varying delays

In this paper, we investigate the problem of stabilization via state feedback and/or state-based switching for switched linear systems with mode-dependent time-varying delays. By using the multiple Lyapunov functional method, we establish sufficient conditions that guarantee the switched system is stabilizable via state feedback and/or switching under time-varying delays with appropriate upper bounds. The main results are presented in terms of linear matrix inequalities (LMIs) which generalize some known results and can be easily tested by using the Matlab’s LMI Tool-box.     

不确定线性时滞中立系统的鲁棒无源控制

研究了带有中立和离散滞后的不确定中立系统的鲁棒无源控制．设计了使不确定时滞中立系统鲁棒稳定和无源的状态反馈控制器．通过线性矩阵不等式和Lyapunov函数，在系统的时间滞后和不确定参数范数有界的约束条件下，给出了系统无源稳定的条件和期望的状态反馈控制器的表达式．最后给出的数值例子说明了设计方法的有效性．     

一类时滞系统的稳定性分析及控制器设计

研究一类具有状态时滞和输入时滞的时变时滞线性系统.首先,通过选取合适的Lyapunov-Krasovskii泛函,应用LMI方法和Lyapunov-Krasovskill稳定性定理对时滞相关的系统进行稳定性分析,并设计了相应的控制器.改进了时变时滞线性系统方面的一些结果.最后用实例验证所得到结果.     

Finite‐time stability of a class of oscillating systems with two delays

In this paper, we study finite‐time stability of an oscillating system with 2 delays. To derive a bounded of state vector, we use a representation of explicit solution involving 2‐delayed matrix polynomial of 2 indices after deriving some fundamental estimates for such delayed matrix polynomial of 2 indices. A sufficient condition is given. Finally, an example is given to demonstrate the application of the main result.     

Stochastic global exponential stability for neutral-type impulsive neural networks with mixed time-delays and Markovian jumping parameters

This paper investigates the problem of the global exponential stability for neutral-type impulsive neural networks with mixed delays and Markovian jumping parameters. The mixed delays include discrete and distributed time-delays and the jumping parameters are generated from a continuous time discrete state homogenous Markov process. Based on the Lyapunov functional, a sufficient criterion is derived in terms of linear matrix equality (LMI).     

State estimation for Markovian jumping genetic regulatory networks with random delays

In this paper, the state estimation problem is investigated for stochastic genetic regulatory networks (GRNs) with random delays and Markovian jumping parameters. The delay considered is assumed to be satisfying a certain stochastic characteristic. Meantime, the delays of GRNs are described by a binary switching sequence satisfying a conditional probability distribution. The aim of this paper is to design a state estimator to estimate the true states of the considered GRNs through the available output measurements. By using Lyapunov functional and some stochastic analysis techniques, the stability criteria of the estimation error systems are obtained in the form of linear matrix inequalities under which the estimation error dynamics is globally asymptotically stable. Then, the explicit expression of the desired estimator is shown. Finally, a numerical example is presented to show the effectiveness of the proposed results.