Finite-time state estimation of stochastic switched delayed neural networks
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- 英文论文题名：Finite-time state estimation of stochastic switched delayed neural networks
- 论文作者：QIU Houming ; CHEN Yun ; YANG Lele
- 英文论文作者：QIU Houming ; CHEN Yun ; YANG Lele ; 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：Switched delayed neural networks ; Stochastic perturbations ; Finite-time ; State estimation ; Average dwell time
- 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：239k
- 原文格式：D
- 会议级别：全国
摘要
The issues of mean-square finite-time stability analysis and state estimator design for stochastic switched delayed neural networks are investigated in this paper. A stability criterion with average dwell time constraint is proposed, such that the mean-square value of state is not larger than a prescribed threshold during a given time interval. Then, a state estimator, which ensures mean-square finite-time stability of an augmented system, is designed. A numerical example is provided to demonstrate the effectiveness of the method.
The issues of mean-square finite-time stability analysis and state estimator design for stochastic switched delayed neural networks are investigated in this paper. A stability criterion with average dwell time constraint is proposed, such that the mean-square value of state is not larger than a prescribed threshold during a given time interval. Then, a state estimator, which ensures mean-square finite-time stability of an augmented system, is designed. A numerical example is provided to demonstrate the effectiveness of the method.
The issues of mean-square finite-time stability analysis and state estimator design for stochastic switched delayed neural networks are investigated in this paper. A stability criterion with average dwell time constraint is proposed, such that the mean-square value of state is not larger than a prescribed threshold during a given time interval. Then, a state estimator, which ensures mean-square finite-time stability of an augmented system, is designed. A numerical example is provided to demonstrate the effectiveness of the method.
引文
[1]S.Haykin.Neural Networks:A Comprehensive Foundation.Prentice-Hall,Englewood Cliffs,1998.
[2]K.Ratchagit.Asymptotic stability of delay-difference system of Hopfield neural networks via matrix inequalities and application.International Journal of Neural Systems,2011,17(5):425-430.
[3]W.Yu,J.Cao,G.Chen.Stability and Hopf bifurcation of a general delayed recurrent neural network.IEEE Transactions on Neural Networks,2008,19(5):845-854.
[4]H.Li,B.Chen,Q.Zhou,W.Qian.Robust stability for uncertain delayed fuzzy Hopfield neural networks with Markovian jumping parameters.IEEE Transactions on Systems Man&Cybernetics Part B,2009,39(1):94-102.
[5]Z.Zeng,W.X.Zheng.Multistability of neural networks with time-varying delays and concave-convex characteristics.IEEE Transactions on Neural Networks and Learning Systems,2012,23(2):293-305.
[6]H.Zhang,F.Yang,X.Liu,Q.Zhang.Stability analysis for neural networks with time-varying delay based on quadratic convex combination.IEEE Transactions on Neural Networks and Learning Systems,2013,24(4):513-521.
[7]Y.Shen,J.Wang.Robustness analysis of global exponential stability of recurrent neural networks in the presence of time delays and random disturbances.IEEE Transactions on Neural Networks and Learning Systems,2012,23(1):87-96.
[8]J.Zhou,S.Xu,B.Zhang,Y.Zou,H.Shen.Robust exponential stability of uncertain stochastic neural networks with distributed delays and reaction Cdiffusions.IEEE Transactions on Neural Networks and Learning Systems,2012,23(9):1407-1416.
[9]J.H.Zhang,P.Shi,J.Qiu.Novel robust stability criteria for uncertain stochastic Hopfield neural networks with timevarying delays.Nonlinear Analysis:Real World Applications,2007,8:1349-1357.
[10]C.Lian,Z.Zeng,W.Yao,H.Tang.Multiple neural networks switched prediction for landslide displacement.Engineering Geology,2015,186:91-99.
[11]X.Y.Lou,B.T.Cui.Delay-dependent criteria for global robust periodicity of uncertain switched recurrent neural networks with time-varying delay.IEEE Transactions on Neural Networks,2008,19(4):549-557.
[12]P.Balasubramaniam,V.Vembarasan,R.Rakkiyappan.Global robust asymptotic stability analysis of uncertain switched Hopfield neural networks with time delay in the leakage term.Neural Computing and Applications,2012,21(7):1593-1616.
[13]X.Wu,Y.Tang,W.Zhang.Stability analysis of switched stochastic neural networks with time-varying delays.Neural Networks,2014,51(2):39-49.
[14]G.P.Chen,Y.Yang,Q.N.Pan.Finite time stability analysis of switched systems with stable and unstable subsystems.Asian Journal of Control,2014,16(4):1224-1228.
[15]R.Ambrosino,F.Calabrese,C.Cosentino,T.G.De.Sufficient conditions for finite-time stability of impulsive dynamical systems.IEEE Transactions on Automatic Control,2009,54(4):861-865.
[16]J.Cheng,H.Zhu,S.Zhong,F.Zheng,Y.Zeng.Finite-time filtering for switched linear systems with a mode-dependent average dwell time.Nonlinear Analysis:Hybrid Systems,2015,15:145-156.
[17]S.Wang,T.Shi,M.Zeng,L.Zhang,F.E.Alsaadi,T.Hayat.New results on robust finite-time boundedness of uncertain switched neural networks with time-varying delays.Neurocomputing,2015,151:522-530.
[18]X.J.Yang,Q.K.Song,Y.R.Liu,Z.J.Zhao.Finite-time stability analysis of fractional-order neural networks with delay.Neurocomputing,2015,152:19-26.
[19]Z.Wang,D.W.C.Ho,X.Liu.State estimation for delayed neural networks.IEEE Transactions on Neural Networks,2005,16:279-284.
[20]Z.Wang,Y.Xu,R.Q.Lu,H.Peng.Finite-time state estimation for coupled markovian neural networks with sensor nonlinearities.IEEE Transactions on Neural Networks and Learning Systems,2017,28:630-638.
[21]T.H.Lee,J.H.Park,O.M.Kwon,S.M.Lee.Stochastic sampled-data control for state estimation of time-varying delayed neural networks.Neural Networks,2013,46(5):99-108.
[22]M.S.Ali,S.Saravanan,S.Arik.Finite-time H∞state estimation for switched neural networks with time-varying delays.Neurocomputing,2016,207:580-589.
[2]K.Ratchagit.Asymptotic stability of delay-difference system of Hopfield neural networks via matrix inequalities and application.International Journal of Neural Systems,2011,17(5):425-430.
[3]W.Yu,J.Cao,G.Chen.Stability and Hopf bifurcation of a general delayed recurrent neural network.IEEE Transactions on Neural Networks,2008,19(5):845-854.
[4]H.Li,B.Chen,Q.Zhou,W.Qian.Robust stability for uncertain delayed fuzzy Hopfield neural networks with Markovian jumping parameters.IEEE Transactions on Systems Man&Cybernetics Part B,2009,39(1):94-102.
[5]Z.Zeng,W.X.Zheng.Multistability of neural networks with time-varying delays and concave-convex characteristics.IEEE Transactions on Neural Networks and Learning Systems,2012,23(2):293-305.
[6]H.Zhang,F.Yang,X.Liu,Q.Zhang.Stability analysis for neural networks with time-varying delay based on quadratic convex combination.IEEE Transactions on Neural Networks and Learning Systems,2013,24(4):513-521.
[7]Y.Shen,J.Wang.Robustness analysis of global exponential stability of recurrent neural networks in the presence of time delays and random disturbances.IEEE Transactions on Neural Networks and Learning Systems,2012,23(1):87-96.
[8]J.Zhou,S.Xu,B.Zhang,Y.Zou,H.Shen.Robust exponential stability of uncertain stochastic neural networks with distributed delays and reaction Cdiffusions.IEEE Transactions on Neural Networks and Learning Systems,2012,23(9):1407-1416.
[9]J.H.Zhang,P.Shi,J.Qiu.Novel robust stability criteria for uncertain stochastic Hopfield neural networks with timevarying delays.Nonlinear Analysis:Real World Applications,2007,8:1349-1357.
[10]C.Lian,Z.Zeng,W.Yao,H.Tang.Multiple neural networks switched prediction for landslide displacement.Engineering Geology,2015,186:91-99.
[11]X.Y.Lou,B.T.Cui.Delay-dependent criteria for global robust periodicity of uncertain switched recurrent neural networks with time-varying delay.IEEE Transactions on Neural Networks,2008,19(4):549-557.
[12]P.Balasubramaniam,V.Vembarasan,R.Rakkiyappan.Global robust asymptotic stability analysis of uncertain switched Hopfield neural networks with time delay in the leakage term.Neural Computing and Applications,2012,21(7):1593-1616.
[13]X.Wu,Y.Tang,W.Zhang.Stability analysis of switched stochastic neural networks with time-varying delays.Neural Networks,2014,51(2):39-49.
[14]G.P.Chen,Y.Yang,Q.N.Pan.Finite time stability analysis of switched systems with stable and unstable subsystems.Asian Journal of Control,2014,16(4):1224-1228.
[15]R.Ambrosino,F.Calabrese,C.Cosentino,T.G.De.Sufficient conditions for finite-time stability of impulsive dynamical systems.IEEE Transactions on Automatic Control,2009,54(4):861-865.
[16]J.Cheng,H.Zhu,S.Zhong,F.Zheng,Y.Zeng.Finite-time filtering for switched linear systems with a mode-dependent average dwell time.Nonlinear Analysis:Hybrid Systems,2015,15:145-156.
[17]S.Wang,T.Shi,M.Zeng,L.Zhang,F.E.Alsaadi,T.Hayat.New results on robust finite-time boundedness of uncertain switched neural networks with time-varying delays.Neurocomputing,2015,151:522-530.
[18]X.J.Yang,Q.K.Song,Y.R.Liu,Z.J.Zhao.Finite-time stability analysis of fractional-order neural networks with delay.Neurocomputing,2015,152:19-26.
[19]Z.Wang,D.W.C.Ho,X.Liu.State estimation for delayed neural networks.IEEE Transactions on Neural Networks,2005,16:279-284.
[20]Z.Wang,Y.Xu,R.Q.Lu,H.Peng.Finite-time state estimation for coupled markovian neural networks with sensor nonlinearities.IEEE Transactions on Neural Networks and Learning Systems,2017,28:630-638.
[21]T.H.Lee,J.H.Park,O.M.Kwon,S.M.Lee.Stochastic sampled-data control for state estimation of time-varying delayed neural networks.Neural Networks,2013,46(5):99-108.
[22]M.S.Ali,S.Saravanan,S.Arik.Finite-time H∞state estimation for switched neural networks with time-varying delays.Neurocomputing,2016,207:580-589.