Finite-time passivity of discrete Markov jump neural networks
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- 英文论文题名：Finite-time passivity of discrete Markov jump neural networks
- 论文作者：YANG Lele ; CHEN Yun ; QIU Houming
- 英文论文作者：YANG Lele ; CHEN Yun ; QIU Houming ; Institute of Information and Control ; Hangzhou Dianzi University
- 年：2017
- 作者机构：Institute of Information and Control,Hangzhou Dianzi University;
- 英文论文关键词：Markov jump neural networks ; Finite-time boundedness ; Passivity ; Nonlinear sensor perturbations ; Delay
- 会议召开时间：2017-07-26
- 会议录名称：第36届中国控制会议论文集（B）
- 英文会议录名称：Proceedings of the 36th Chinese Control Conference（B）
- 语种：英文
- 分类号：TP183
- 学会代码：KZLL
- 会议名称：第36届中国控制会议
- 会议地点：中国辽宁大连
- 主办单位：中国自动化学会控制理论专业委员会
- 学会名称：中国自动化学会控制理论专业委员会
- 页数：6
- 文件大小：225k
- 原文格式：D
- 会议级别：全国
摘要
This paper investigates the problem of mean square finite-time passivity for discrete-time Markov jump neural networks with time delays and stochastic perturbations. In the measurement equation, a class of sensor nonlinearities are considered,which cover the standard Lipschitz condition as a special one. A sufficient mean square finite-time boundedness condition is established based on Lyapunov-like functional method. The mean square finite-time passivity result for the system with nonlinear sensor perturbations is then obtained. A numerical example is presented to show the applicability of the proposed results.
This paper investigates the problem of mean square finite-time passivity for discrete-time Markov jump neural networks with time delays and stochastic perturbations. In the measurement equation, a class of sensor nonlinearities are considered,which cover the standard Lipschitz condition as a special one. A sufficient mean square finite-time boundedness condition is established based on Lyapunov-like functional method. The mean square finite-time passivity result for the system with nonlinear sensor perturbations is then obtained. A numerical example is presented to show the applicability of the proposed results.
This paper investigates the problem of mean square finite-time passivity for discrete-time Markov jump neural networks with time delays and stochastic perturbations. In the measurement equation, a class of sensor nonlinearities are considered,which cover the standard Lipschitz condition as a special one. A sufficient mean square finite-time boundedness condition is established based on Lyapunov-like functional method. The mean square finite-time passivity result for the system with nonlinear sensor perturbations is then obtained. A numerical example is presented to show the applicability of the proposed results.
引文
[1]J.D.Cao,K.Yuan,H.X.Li,Global asymptotical stability of recurrent neural networks with multiple discrete delays and distributed delays,IEEE Trans.Neural Networks,17(6):1646–1651,2006.
[2]V.N.Phat,H.Trinh,Exponential stabilization of neural networks with various activation functions and mixed timevarying delays,IEEE Trans.Neural Networks,21:1180–1184,2010.
[3]C.Xu,Q.Zhang,Existence and global exponential stability of anti-periodic solutions for BAM neural networks with inertial term and delay,Neurocomputing,153:108–116,2015.
[4]Z.G.Wu,H.Y.Su,J.Chu,Improved delay-dependent stability condition of discrete recurrent neural networks with timevarying delays,IEEE Trans.Neural Networks,21:692–697,2010.
[5]D.Zhang,L.Yu,Q.G.Wang,C.J.Ong,Estimator design for discrete-time switched neural networks with asynchronous switching and time-varying delay,IEEE Trans.Neural Netw.Learn.Syst,23:827–834,2012.
[6]L.Wu,X.Su,P.Shi,Output feedback control of Markovian jump repeated scalar nonlinear systems,IEEE Trans.Autom.Control,59(1):199–204,2014.
[7]Y.Chen,W.X.Zheng,L2-L∞filtering for stochastic Markovian jump delay systems with nonlinear perturbations,Signal Processing,109:154–164,2015.
[8]M.J.Park,O.M.Kwon,J.H.Park,S.M.Lee,E.J.Cha,H∞state estimation for discrete-time neural networks with interval time-varying delays and probabilistic diverging disturbances,Neurocomputing,153:255–270,2015.
[9]Y.Zhang,S.Xu,Z.Zeng,Novel robust stability criteria of discrete-time stochstic recurrent neural networks with time delay,Neurocomputing,72:3343–3351,2009.
[10]W.Luo,K.Zhong,S.Zhu,Y.Shen,Further results on robustness analysis of global exponential stability of recurrent neural networks with time delays and random disturbances,Neural Networks,53:127–133,2014.
[11]D.Y.Wang,L.S.Li,Mean-square stability analysis of discrete-time stochastic Markov jump recurrent neural networks with mixed delays,Neurocomputing,189:171–178,2016.
[12]J.C.Geromel,P.Colaneri,P.Bolzern,Passivity of switched linear systems:Analysis and control design,Systems&Control Letters,61:549–554,2012.
[13]J.Li,J.Zhao,Passivity and feedback passification of switched discrete-time linear systems,Systems&Control Letters,62:1073–1081,2013.
[14]Y.Q.Wu,H.Y.Su,R.Q.Lu,Z.G.Wu,Z.Shu,Passivity-based non-fragile control for Markovian jump systems with aperiodic sampling,Systems&Control Letters,84:35–43,2015.
[15]S.Zhu,Y.Shen,Passivity analysis of stochastic delayed neural networks with Markovian switching,Neurocomputing,74:1754–1761,2011.
[16]Y.J.Shu,X.G.Liu,Y.J.Liu,S.M.Lee,E.J.Cha,Stability and passivity analysis for uncertain discrete-time neural networks with time-varying delay,Neurocomputing,173:1706–1714,2016.
[17]Q.K.Song,J.L.Liang,Z.D.Wang,Passivity analysis of discrete-time stochastic neural networks with time-varying delays,Neurocomputing,72:1782–1788,2009.
[18]S.Senthilraj,R.Raja,Q.X.Zhu,R.Samidurai,Z.S.Yao,Delay-interval-dependent passivity analysis of stochastic neural networks with Markovian jumping parameters and time delay in the leakage term,Nonlinear Analysis:Hybrid Systems,22:262–275,2016.
[19]K.Mathiyalagan,J.H.Park,R.Sakthivel,Novel results on robust finite-time passivity for discrete-time delayed neural networks,Neurocomputing,177:585–593,2016.
[2]V.N.Phat,H.Trinh,Exponential stabilization of neural networks with various activation functions and mixed timevarying delays,IEEE Trans.Neural Networks,21:1180–1184,2010.
[3]C.Xu,Q.Zhang,Existence and global exponential stability of anti-periodic solutions for BAM neural networks with inertial term and delay,Neurocomputing,153:108–116,2015.
[4]Z.G.Wu,H.Y.Su,J.Chu,Improved delay-dependent stability condition of discrete recurrent neural networks with timevarying delays,IEEE Trans.Neural Networks,21:692–697,2010.
[5]D.Zhang,L.Yu,Q.G.Wang,C.J.Ong,Estimator design for discrete-time switched neural networks with asynchronous switching and time-varying delay,IEEE Trans.Neural Netw.Learn.Syst,23:827–834,2012.
[6]L.Wu,X.Su,P.Shi,Output feedback control of Markovian jump repeated scalar nonlinear systems,IEEE Trans.Autom.Control,59(1):199–204,2014.
[7]Y.Chen,W.X.Zheng,L2-L∞filtering for stochastic Markovian jump delay systems with nonlinear perturbations,Signal Processing,109:154–164,2015.
[8]M.J.Park,O.M.Kwon,J.H.Park,S.M.Lee,E.J.Cha,H∞state estimation for discrete-time neural networks with interval time-varying delays and probabilistic diverging disturbances,Neurocomputing,153:255–270,2015.
[9]Y.Zhang,S.Xu,Z.Zeng,Novel robust stability criteria of discrete-time stochstic recurrent neural networks with time delay,Neurocomputing,72:3343–3351,2009.
[10]W.Luo,K.Zhong,S.Zhu,Y.Shen,Further results on robustness analysis of global exponential stability of recurrent neural networks with time delays and random disturbances,Neural Networks,53:127–133,2014.
[11]D.Y.Wang,L.S.Li,Mean-square stability analysis of discrete-time stochastic Markov jump recurrent neural networks with mixed delays,Neurocomputing,189:171–178,2016.
[12]J.C.Geromel,P.Colaneri,P.Bolzern,Passivity of switched linear systems:Analysis and control design,Systems&Control Letters,61:549–554,2012.
[13]J.Li,J.Zhao,Passivity and feedback passification of switched discrete-time linear systems,Systems&Control Letters,62:1073–1081,2013.
[14]Y.Q.Wu,H.Y.Su,R.Q.Lu,Z.G.Wu,Z.Shu,Passivity-based non-fragile control for Markovian jump systems with aperiodic sampling,Systems&Control Letters,84:35–43,2015.
[15]S.Zhu,Y.Shen,Passivity analysis of stochastic delayed neural networks with Markovian switching,Neurocomputing,74:1754–1761,2011.
[16]Y.J.Shu,X.G.Liu,Y.J.Liu,S.M.Lee,E.J.Cha,Stability and passivity analysis for uncertain discrete-time neural networks with time-varying delay,Neurocomputing,173:1706–1714,2016.
[17]Q.K.Song,J.L.Liang,Z.D.Wang,Passivity analysis of discrete-time stochastic neural networks with time-varying delays,Neurocomputing,72:1782–1788,2009.
[18]S.Senthilraj,R.Raja,Q.X.Zhu,R.Samidurai,Z.S.Yao,Delay-interval-dependent passivity analysis of stochastic neural networks with Markovian jumping parameters and time delay in the leakage term,Nonlinear Analysis:Hybrid Systems,22:262–275,2016.
[19]K.Mathiyalagan,J.H.Park,R.Sakthivel,Novel results on robust finite-time passivity for discrete-time delayed neural networks,Neurocomputing,177:585–593,2016.