In this paper, new delay-dependent stability criteria for asymptotic stability of neural networks with time-varying delays are derived. The stability conditions are represented in terms of linear matrix inequalities ...In this paper, new delay-dependent stability criteria for asymptotic stability of neural networks with time-varying delays are derived. The stability conditions are represented in terms of linear matrix inequalities （LMIs） by constructing new Lyapunov-Krasovskii functional. The proposed functional has an augmented quadratic form with states as well as the nonlinear function to consider the sector and the slope constraints. The less conservativeness of the proposed stability criteria can be guaranteed by using convex properties of the nonlinear function which satisfies the sector and slope bound. Numerical examples are presented to show the effectiveness of the proposed method.展开更多
This paper concernes analysis for the global exponential stability of a class of recurrent neural networks with mixed discrete and distributed delays. It first proves the existence and uniqueness of the balance point,...This paper concernes analysis for the global exponential stability of a class of recurrent neural networks with mixed discrete and distributed delays. It first proves the existence and uniqueness of the balance point, then by employing the Lyapunov-Krasovskii functional and Young inequality, it gives the sufficient condition of global exponential stability of cellular neural network with mixed discrete and distributed delays, in addition, the example is provided to illustrate the applicability of the result.展开更多
Two easily verified delay-dependent criteria of mean-square exponential robust stability are obtained by constructing Lyapunov-Krasovskii functional and employing the decomposition technique of the continuous matrix-d...Two easily verified delay-dependent criteria of mean-square exponential robust stability are obtained by constructing Lyapunov-Krasovskii functional and employing the decomposition technique of the continuous matrix-discovered set of grey matrix and Ito formula.A numerical example shows the validity and practicality of the criteria presented in this paper.展开更多
In order to analyze the capacity stability of the time-varying-propagation and delay-dependent of mobile ad-hoc networks （MANETs）, in this paper, a novel approach is proposed to explore the capacity asymptotic stabi...In order to analyze the capacity stability of the time-varying-propagation and delay-dependent of mobile ad-hoc networks （MANETs）, in this paper, a novel approach is proposed to explore the capacity asymptotic stability for the delay- dependent of MANETs based on non-cooperative game theory, where the delay-dependent conditions are explicitly taken into consideration. This approach is based on the Lyapunov-Krasovskii stability theory for functional differential equations and the linear matrix inequality （LMI） technique. A corresponding Lyapunov-Krasovskii functional is introduced for the stability analysis of this system with use of the descriptor and ＂neutral-type＂ model transformation without producing any additional dynamics. The delay-dependent stability criteria are derived for this system. Conditions are given in terms of linear matrix inequalities, and for the first time referred to neutral systems with the time-varying propagation and delay- dependent stability for capacity analysis of MANETs. The proposed criteria are less conservative since they are based on an equivalent model transformation. Furthermore, we also provide an effective and efficient iterative algorithm to solve the constrained stability control model. Simulation experiments have verified the effectiveness and efficiency of our algorithm.展开更多
In this paper, we have improved delay-dependent stability criteria for recurrent neural networks with a delay varying over a range and Markovian jumping parameters. The criteria improve over some previous ones in that...In this paper, we have improved delay-dependent stability criteria for recurrent neural networks with a delay varying over a range and Markovian jumping parameters. The criteria improve over some previous ones in that they have fewer matrix variables yet less conservatism. In addition, a numerical example is provided to illustrate the applicability of the result using the linear matrix inequality toolbox in MATLAB.展开更多
This paper studies the problem of linear matrix inequality （LMI） approach to robust stability analysis for stochastic neural networks with a time-varying delay. By developing a delay decomposition approach, the info...This paper studies the problem of linear matrix inequality （LMI） approach to robust stability analysis for stochastic neural networks with a time-varying delay. By developing a delay decomposition approach, the information of the delayed plant states can be taken into full consideration. Based on the new Lyapunov-Krasovskii functional, some inequality techniques and stochastic stability theory, new delay-dependent stability criteria are obtained in terms of LMIs. The proposed results prove the less conservatism, which are realized by choosing new Lyapunov matrices in the decomposed integral intervals. Finally, numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed LMI method.展开更多
The problem of the global exponential robust stability of interval neural networks with a fixed delay was studied by an approach combining the Lyapunov-Krasovskii functional with the linear matrix inequality (LMI). Th...The problem of the global exponential robust stability of interval neural networks with a fixed delay was studied by an approach combining the Lyapunov-Krasovskii functional with the linear matrix inequality (LMI). The results obtained provide an easily verified guideline for determining the exponential robust stability of delayed neural networks. The theoretical analysis and numerical simulations show that the results are less conservative and less restrictive than those reported recently in the literature.展开更多
基金Project supported by the Daegu University Research Grant,2009
文摘In this paper, new delay-dependent stability criteria for asymptotic stability of neural networks with time-varying delays are derived. The stability conditions are represented in terms of linear matrix inequalities （LMIs） by constructing new Lyapunov-Krasovskii functional. The proposed functional has an augmented quadratic form with states as well as the nonlinear function to consider the sector and the slope constraints. The less conservativeness of the proposed stability criteria can be guaranteed by using convex properties of the nonlinear function which satisfies the sector and slope bound. Numerical examples are presented to show the effectiveness of the proposed method.
基金Project supported by the National Natural Science Foundations of China(Grant No.70871056)the Society Science Foundation from Ministry of Education of China(Grant No.08JA790057)the Advanced Talents'Foundation and Student's Foundation of Jiangsu University,China(Grant Nos.07JDG054 and 07A075)
文摘This paper concernes analysis for the global exponential stability of a class of recurrent neural networks with mixed discrete and distributed delays. It first proves the existence and uniqueness of the balance point, then by employing the Lyapunov-Krasovskii functional and Young inequality, it gives the sufficient condition of global exponential stability of cellular neural network with mixed discrete and distributed delays, in addition, the example is provided to illustrate the applicability of the result.
基金Supported by the Natural Science Foundation of Henan Province（061105440） Supported by the Natural Science Foundation of the Education Department of Henan Province（2008A1100150）
文摘Two easily verified delay-dependent criteria of mean-square exponential robust stability are obtained by constructing Lyapunov-Krasovskii functional and employing the decomposition technique of the continuous matrix-discovered set of grey matrix and Ito formula.A numerical example shows the validity and practicality of the criteria presented in this paper.
基金supported by the National Natural Science Foundation of China(Grant No.61173131)the Fundamental Research Funds for the Interdisciplinary ClassMajor Projects for the Central Universities of Ministry of Education of China(Grant No.CDJZR12098801)+3 种基金the Based and Advanced Research Projects ofChongqing of China(Grant No.cstc2013jcyjA40033)the Key Strategic Project of Chongqing of China(Grant No.CSTC2009AB2230)the Strategeic Projectof Chongqing of China(Grant No.2009AC2057)the Special Funding for Scientific Research Project of Chongqing Postdoctoral Researchers,China(Grant No.XM20120054)
文摘In order to analyze the capacity stability of the time-varying-propagation and delay-dependent of mobile ad-hoc networks （MANETs）, in this paper, a novel approach is proposed to explore the capacity asymptotic stability for the delay- dependent of MANETs based on non-cooperative game theory, where the delay-dependent conditions are explicitly taken into consideration. This approach is based on the Lyapunov-Krasovskii stability theory for functional differential equations and the linear matrix inequality （LMI） technique. A corresponding Lyapunov-Krasovskii functional is introduced for the stability analysis of this system with use of the descriptor and ＂neutral-type＂ model transformation without producing any additional dynamics. The delay-dependent stability criteria are derived for this system. Conditions are given in terms of linear matrix inequalities, and for the first time referred to neutral systems with the time-varying propagation and delay- dependent stability for capacity analysis of MANETs. The proposed criteria are less conservative since they are based on an equivalent model transformation. Furthermore, we also provide an effective and efficient iterative algorithm to solve the constrained stability control model. Simulation experiments have verified the effectiveness and efficiency of our algorithm.
基金Project supported by the National Natural Science Foundation of China (Grant No.60674026)the Jiangsu Provincial Natural Science Foundation of China (Grant No.BK2007016)
文摘In this paper, we have improved delay-dependent stability criteria for recurrent neural networks with a delay varying over a range and Markovian jumping parameters. The criteria improve over some previous ones in that they have fewer matrix variables yet less conservatism. In addition, a numerical example is provided to illustrate the applicability of the result using the linear matrix inequality toolbox in MATLAB.
基金supported by the Science Foundation of the Department of Science and Technology,New Delhi,India (Grant No.SR/S4/MS:485/07)
文摘This paper studies the problem of linear matrix inequality （LMI） approach to robust stability analysis for stochastic neural networks with a time-varying delay. By developing a delay decomposition approach, the information of the delayed plant states can be taken into full consideration. Based on the new Lyapunov-Krasovskii functional, some inequality techniques and stochastic stability theory, new delay-dependent stability criteria are obtained in terms of LMIs. The proposed results prove the less conservatism, which are realized by choosing new Lyapunov matrices in the decomposed integral intervals. Finally, numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed LMI method.
文摘The problem of the global exponential robust stability of interval neural networks with a fixed delay was studied by an approach combining the Lyapunov-Krasovskii functional with the linear matrix inequality (LMI). The results obtained provide an easily verified guideline for determining the exponential robust stability of delayed neural networks. The theoretical analysis and numerical simulations show that the results are less conservative and less restrictive than those reported recently in the literature.
