Global finite-time stabilization of memristor-based neural networks with time-varying delays via hybrid control
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- 英文论文题名：Global finite-time stabilization of memristor-based neural networks with time-varying delays via hybrid control
- 论文作者：Yinfang Song
- 英文论文作者：Yinfang Song ; School of Information and Mathematics ; Yangtze University
- 年：2017
- 作者机构：School of Information and Mathematics,Yangtze University;
- 英文论文关键词：Finite-time stabilization ; Impulsive control ; Nonlinear feedback control ; Memristor-based neural networks ; Lyapunov function approach
- 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（A）
- 英文会议录名称：Proceedings of the 36th Chinese Control Conference（A）
- 语种：英文
- 分类号：O231;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：8
- 文件大小：233k
- 原文格式：D
- 会议级别：全国
摘要
In this paper, the problem of finite-time stabilization for a class of memristor-based neural networks with time-varying delays is investigated by using hybrid impulsive and nonlinear feedback controllers. Based on the theory of the differential equations with discontinuous right and Lyapunov function approach, several sufficient conditions are derived to guarantee the finite-time stabilization of memristor-based neural networks. Especially, the existing criteria are improved since the impulsive control is introduced in the convergence time. Finally, the effectiveness of the obtained results is illustrated by two numerical examples.
In this paper, the problem of finite-time stabilization for a class of memristor-based neural networks with time-varying delays is investigated by using hybrid impulsive and nonlinear feedback controllers. Based on the theory of the differential equations with discontinuous right and Lyapunov function approach, several sufficient conditions are derived to guarantee the finite-time stabilization of memristor-based neural networks. Especially, the existing criteria are improved since the impulsive control is introduced in the convergence time. Finally, the effectiveness of the obtained results is illustrated by two numerical examples.
In this paper, the problem of finite-time stabilization for a class of memristor-based neural networks with time-varying delays is investigated by using hybrid impulsive and nonlinear feedback controllers. Based on the theory of the differential equations with discontinuous right and Lyapunov function approach, several sufficient conditions are derived to guarantee the finite-time stabilization of memristor-based neural networks. Especially, the existing criteria are improved since the impulsive control is introduced in the convergence time. Finally, the effectiveness of the obtained results is illustrated by two numerical examples.
引文
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[2]D.B.Strukov,G.S.Snider,G.R.Stewart,R.S.Williams,The missing memristor found,Nature,453:80-83,2008.
[3]Z.Guo,J.Wang,Y.Zhen,Global exponential dissipativity and stabilization of memristor-based recurrent neural networks with time-varying delays,Neural Netw.,48(6):158-172,2013.
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[5]F.Corinto,A.ASCOLI,M.Gilli,Analysis of current-voltage characteristics for memristive elements in pattern recognition systems,Int.J.Circuit Theory Appl.,40(12):1277-1320,2012.
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[10]A.Wu,S.Wen,Z.Zeng,Synchronization control of a class of memristor-based recurrent neural networks,Inf.Sci.,183(1),106-116,2012.
[11]A.Wu,Z.Zeng,Exponential stabilization of memristive neural networks with time delays,IEEE Trans.Neural Netw.Learn.Syst.,23(12):1919-1929,2012.
[12]S.Wen,Z.Zeng,T.Huang,Y.Chen,Passivity analysis of memristor-based recurrent neural networks with time-varying delays,J.Frank.Inst.,350(8):2354-2370,2013.
[13]S.Wen,T.Huang,Z.Zeng,Y.Chen,P.Li,Circuit design and exponential stabilization of memristive neural networks,Neural Netw.,63:48-56,2015.
[14]G.Zhang,Y.Shen,Q.Yin,J.Sun,Global exponential periodicity and stability of a class of memristor-based recurrent neural networks with multiple delays,Inf.Sci.,232(232):386-396,2013.
[15]Y.Gu,Y.Yu,H.Wang,Synchronization for fractional-order time-delayed memristor-based neural networks with parameter uncertainty,J.Frank.Inst.,353(15):3657-3684,2016.
[16]Z.Guo,J.Wang,Z.Yan,Attractivity analysis of memristorbased cellular neural networks with time-varying delays,IEEE Trans.Neural Netw.Learn.Syst.,25(4):704-717,2013.
[17]L.Chen,R.Wu,J.Cao,J.Liu,Stability and synchronization of memristor-based fractional-order delayed neural networks,Neural Netw.,71:37-44,2015.
[18]X.Yang,D.W.C.Ho,Synchronization of delayed memristive neural networks:robust analysis approach,IEEE Trans.Cybern.,99:1-11,2015.
[19]M.Jiang,S.Wang,J.Mei,Y.Shen,Finite-time synchronization control of a class of memristor-based recurrent neural networks,Neural Netw.,63:133-140,2015.
[20]A.Abdurahman,H.Jiang,Z.Teng,Finite-time synchronizatin for memristor-based neural networks with time-varying delays,Neural Netw.,69:20-28,2015.
[21]X.Liu,Q.Wang,Impulsive stabilization of high-order Hopfield-type neural networks with time-varying delays,IEEE Trans.Neural Netw.Learn.Syst.,19(1):71-79,2008.
[22]Y.Shen,J.Wang,Noise-induced stabilization of the recurrent neural networks with mixed time-varying delays and Markovian-switching parameters,IEEE Trans.Neural Netw.Learn.Syst.,18(6):1857-1862,2007.
[23]G.Zhang,Y.Shen,Exponential stabilization of memristorbased chaotic neural networks with time-varying delays via imtermittent control,IEEE Trans.Neural Netw.Learn.Syst.,26(7):1431-1441,2015.
[24]Z.Wang,Y.Liu,Guo.Wei,X.Liu,A note on control of a class of discrete-time stochastic systems with distributed delays and nonlinear disturbances,Automatica,46(3):543-548,2010.
[25]J.Lu;D.W.C.Ho,Z.Wang,Pinning stabilization of linearly coupled stochastic neural networks via minimum number of controllers,IEEE Trans.Neural Netw.,20(10):1617-1629,2009.
[26]X.Liu,J.H.Park,N.Jiang,J.Cao,Nonsmooth finite-time stabilization of neural networks with discontinuous activations,Neural Netw.,52:25-32,2014.
[27]Z.Cai,L.Huang,M.Zhu,D.Wang,Finite-time stabilization control of memristor-based neural networks,Nonlinear Nonlinear Anal.Hybrid Syst.,20:37-54,2016.
[28]L.Wang,Y.Shen,Finite-time stabilizability and instabilizability of delayed memristive neuralnetworks with nonlinear discontinuous controller,IEEE Trans.Neural Netw.Learn.Syst.,26(11):2194-2924 2015.
[29]J.Mei,M.Jiang,W.Xu,B.Wang,Finite-time synchronization control of complex dynamical networks with time delay,Commun Nonlinear Sci Numer Simulat.,18(9):2462-2478,2013.
[30]S.Duan,H.Wang,L.Wang,T.Huang,C.Li,Impulsive effects and stability analysis on memristive neural networks with variable delays,IEEE Trans.Neural Netw.Learn.Syst.,28(2):476-481,2017.
[2]D.B.Strukov,G.S.Snider,G.R.Stewart,R.S.Williams,The missing memristor found,Nature,453:80-83,2008.
[3]Z.Guo,J.Wang,Y.Zhen,Global exponential dissipativity and stabilization of memristor-based recurrent neural networks with time-varying delays,Neural Netw.,48(6):158-172,2013.
[4]A.Abdurahman,H.Jiang,Z.Teng,Exponential lag synchronization for memristor-based neural networks with mixed time delays via hyrid switching control,J.Frank.Inst.,353(13):2859-2880,2016.
[5]F.Corinto,A.ASCOLI,M.Gilli,Analysis of current-voltage characteristics for memristive elements in pattern recognition systems,Int.J.Circuit Theory Appl.,40(12):1277-1320,2012.
[6]S.Shin,K.Kim,S.Kang,Memristor applications for programmable analog ICs,IEEE Trans.Nanotechnol,10(2):266-274,2011.
[7]H.Yan,H.Choe,S.Nam,Y.Hu,J.Klemic,J.Ellenbogen,C.Liber,Programmable nanowire circuits for nanoprocessors,Nature,470(7333):240-244,2011.
[8]J.Yang,D.Strukov,D.Stewart,Memristive devices for computing,Nat.Nanotechnol,8(1):13-24,2013.
[9]S.Kvatinsky,E.G.Friedman,A.Kolodny,U.C.Weiser,Team:threshold adaptive memristor model,IEEE Trans.Circuits Syst.I,Reg.Paper,60(1):211-221,2013.
[10]A.Wu,S.Wen,Z.Zeng,Synchronization control of a class of memristor-based recurrent neural networks,Inf.Sci.,183(1),106-116,2012.
[11]A.Wu,Z.Zeng,Exponential stabilization of memristive neural networks with time delays,IEEE Trans.Neural Netw.Learn.Syst.,23(12):1919-1929,2012.
[12]S.Wen,Z.Zeng,T.Huang,Y.Chen,Passivity analysis of memristor-based recurrent neural networks with time-varying delays,J.Frank.Inst.,350(8):2354-2370,2013.
[13]S.Wen,T.Huang,Z.Zeng,Y.Chen,P.Li,Circuit design and exponential stabilization of memristive neural networks,Neural Netw.,63:48-56,2015.
[14]G.Zhang,Y.Shen,Q.Yin,J.Sun,Global exponential periodicity and stability of a class of memristor-based recurrent neural networks with multiple delays,Inf.Sci.,232(232):386-396,2013.
[15]Y.Gu,Y.Yu,H.Wang,Synchronization for fractional-order time-delayed memristor-based neural networks with parameter uncertainty,J.Frank.Inst.,353(15):3657-3684,2016.
[16]Z.Guo,J.Wang,Z.Yan,Attractivity analysis of memristorbased cellular neural networks with time-varying delays,IEEE Trans.Neural Netw.Learn.Syst.,25(4):704-717,2013.
[17]L.Chen,R.Wu,J.Cao,J.Liu,Stability and synchronization of memristor-based fractional-order delayed neural networks,Neural Netw.,71:37-44,2015.
[18]X.Yang,D.W.C.Ho,Synchronization of delayed memristive neural networks:robust analysis approach,IEEE Trans.Cybern.,99:1-11,2015.
[19]M.Jiang,S.Wang,J.Mei,Y.Shen,Finite-time synchronization control of a class of memristor-based recurrent neural networks,Neural Netw.,63:133-140,2015.
[20]A.Abdurahman,H.Jiang,Z.Teng,Finite-time synchronizatin for memristor-based neural networks with time-varying delays,Neural Netw.,69:20-28,2015.
[21]X.Liu,Q.Wang,Impulsive stabilization of high-order Hopfield-type neural networks with time-varying delays,IEEE Trans.Neural Netw.Learn.Syst.,19(1):71-79,2008.
[22]Y.Shen,J.Wang,Noise-induced stabilization of the recurrent neural networks with mixed time-varying delays and Markovian-switching parameters,IEEE Trans.Neural Netw.Learn.Syst.,18(6):1857-1862,2007.
[23]G.Zhang,Y.Shen,Exponential stabilization of memristorbased chaotic neural networks with time-varying delays via imtermittent control,IEEE Trans.Neural Netw.Learn.Syst.,26(7):1431-1441,2015.
[24]Z.Wang,Y.Liu,Guo.Wei,X.Liu,A note on control of a class of discrete-time stochastic systems with distributed delays and nonlinear disturbances,Automatica,46(3):543-548,2010.
[25]J.Lu;D.W.C.Ho,Z.Wang,Pinning stabilization of linearly coupled stochastic neural networks via minimum number of controllers,IEEE Trans.Neural Netw.,20(10):1617-1629,2009.
[26]X.Liu,J.H.Park,N.Jiang,J.Cao,Nonsmooth finite-time stabilization of neural networks with discontinuous activations,Neural Netw.,52:25-32,2014.
[27]Z.Cai,L.Huang,M.Zhu,D.Wang,Finite-time stabilization control of memristor-based neural networks,Nonlinear Nonlinear Anal.Hybrid Syst.,20:37-54,2016.
[28]L.Wang,Y.Shen,Finite-time stabilizability and instabilizability of delayed memristive neuralnetworks with nonlinear discontinuous controller,IEEE Trans.Neural Netw.Learn.Syst.,26(11):2194-2924 2015.
[29]J.Mei,M.Jiang,W.Xu,B.Wang,Finite-time synchronization control of complex dynamical networks with time delay,Commun Nonlinear Sci Numer Simulat.,18(9):2462-2478,2013.
[30]S.Duan,H.Wang,L.Wang,T.Huang,C.Li,Impulsive effects and stability analysis on memristive neural networks with variable delays,IEEE Trans.Neural Netw.Learn.Syst.,28(2):476-481,2017.