随机Markov跳跃系统有限时间稳定性

研究了一类随机线性It(^overo)Markov跳跃系统的有限时间稳定性问题。首先,定义了系统的有限时间随机稳定和有限时间有界;其次,给出了系统有限时间随机稳定的充分必要条件,利用线性矩阵不等式技术设计了状态反馈能稳控制器;最后,通过数值例子和系统仿真验证了方法的有效性。     

Mean square stability of recurrent neural networks with random delay and Markovian switching

To establish easily proved conditions under which the random delayed recurrent neural network with Markovian switching is mean-square stability,the evolution of the delay was modeled by a continuous-time homogeneous Markov process with a finite number of states.By employing Lyapunov-Krasovskii functionals and conducting stochastic analysis,a linear matrix inequality (LMI) approach was developed to derive the criteria for mean-square stability,which can be readily checked by some standard numerical packages such as the Matlab LMI Toolbox.A numerical example was exploited to show the usefulness of the derived LMI-based stability conditions.     

Asymptotical mean square stability of cellular neural networks with random delay

In this paper,the asymptotical mean-square stability analysis problem is considered for a class of cellular neural networks (CNNs) with random delay. Compared with the previous work,the delay is modeled by a continuous-time homogeneous Markov process with a finite number of states. The main purpose of this paper is to establish easily verifiable conditions under which the random delayed cellular neural network is asymptotic mean-square stability. By using some stochastic analysis techniques and Lyapunov-Krasovskii functional,some conditions are derived to ensure that the cellular neural networks with random delay is asymptotical mean-square stability. A numerical example is exploited to show the vadlidness of the established results.     

A novel LMI criterion for power system stability with multiple time-delays

This paper presents a novel LMI criterion for electric power system stability with multiple time-delays.Initially,the linear time-invariant model of the power system with multiple delays is constructed,subsequently,the former criteria and the novel criterion of this paper are demonstrated in this paper,and the novel criterion is fully proved according to Lyapunov direct method.Specifically,the proposed criterion utilizes a properly simplified Lyapunov-Krasovskii functional,and no free-weighting matrix is introduced in the formation of new criterion,as a consequence,the calculation efficiency is remarkably enhanced.A typical second-order delay system,a single-generator-infinite-bus system and the WSCC 3-generator-9-bus delay system are taken to validate the effectiveness and efficiency enhancement of the proposed criterion.The numerical results indicate that the criterion of this paper can generate the same stability margin with the former ones.Further,the numerical results also verify that the proposed criterion’s efficiency is substantially boosted and calculation time is greatly curtailed.     

多时变时滞细胞神经网络的全局渐近稳定

研究了多时变时滞细胞神经网络的全局渐近稳定问题。给出了多时变时滞细胞神经网络平衡点唯一性和全局渐近稳定的几个新充分判据。所研究的网络模型扩展了现有的同类网络模型,而且所得到的这些新稳定判据考虑了神经元激励和抑制的影响,且能够表示成线性矩阵不等式的形式,易于用现有的内点算法等方法验证。通过2个注释说明了所得结果的有效性。     

具有时滞的Hopfield神经网络的渐近稳定性

应用Lyapunov泛函理论和矩阵分析技巧研究具有时滞的Hopfield神经网络的渐近稳定性问题,获得了两个基于线性矩阵不等式的判据。数值示例表明所得结果比已有文献中的结果更少保守。     

变时滞神经网络的时滞相关全局渐近稳定新判据

研究了变时滞神经网络的时滞相关渐近稳定性问题。利用Lyapunov-Krasovskii泛函和积分等式方法,提出了此类神经网络有唯一稳定点和全局渐近稳定的充分条件。与已有文献相比,不再要求激活函数单调,并且时滞导数可以不小于1,具有较弱的保守性。算例验证了所给方法的正确性。     

时变时滞双向联想记忆神经网络的鲁棒稳定性

通过构造适当的Lyapunov泛函分析了一类时变时滞双向联想记忆神经网络的平衡点稳定性问题。不要求激励函数的单调性和可微性,得到了保证时变时滞双向联想记忆神经网络的平衡点全局鲁棒渐近稳定的两个新判据。所得到的结果能够表示成线性矩阵不等式形式,进而易于用内点算法等方法来验证。通过仿真例子与其他文献中的一些结果进行比较,表明了本文所得结果的有效性。     

随机分布参数切换系统的输入输出有限时间稳定与镇定

本文研究了一类随机分布参数切换系统的输入输出有限时间稳定问题.首先,给出了系统在均方意义下输入输出有限时间稳定的概念.其次,通过利用分片Lyapunov-Krasovskii函数、平均驻留时间及线性矩阵不等式等工具,建立了随机分布参数切换系统输入输出有限时间稳定的充分条件;设计一个状态反馈控制器确保相应的闭环系统输入输出有限时间稳定,并以严格的数学推导加以证明.最后,通过两个仿真例子验证了所得结果的有效性.     

Global stability of interval recurrent neural networks

The robust global exponential stability of a class of interval recurrent neural networks(RNNs) is studied,and a new robust stability criterion is obtained in the form of linear matrix inequality.The problem of robust stability of interval RNNs is transformed into a problem of solving a class of linear matrix inequalities.Thus,the robust stability of interval RNNs can be analyzed by directly using the linear matrix inequalities(LMI) toolbox of MATLAB.Numerical example is given to show the effectiveness of the obtained results.