Sampled-data H_∞ filter for delayed neural network subject to sensor saturation
详细信息 查看官网全文
- 英文论文题名：Sampled-data H_∞ filter for delayed neural network subject to sensor saturation
- 论文作者：Liuwen Li ; Wenlin Zou ; Shumin Fei ; Jie Cao ; Pan Wang
- 英文论文作者：Liuwen Li ; Wenlin Zou ; Shumin Fei ; Jie Cao ; Pan Wang ; Key Laboratory of Measurement and Control of CSE (Ministry of Education) ; School of Automation ; Southeast University ; School of Information Engineering ; Nanjing Xiaozhuang University ; School of Computer Science and Engineering ; Nanjing University of Science and Technology
- 年：2017
- 作者机构：Key Laboratory of Measurement and Control of CSE (Ministry of Education),School of Automation,Southeast University;School of Information Engineering,Nanjing Xiaozhuang University;School of Computer Science and Engineering,Nanjing University of Science and Technology;
- 英文论文关键词：sampled-data systems ; sensor saturation ; H_∞ filter ; delayed neural network
- 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：TN713;TP183;TP212
- 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：164k
- 原文格式：D
- 会议级别：全国
摘要
This paper is concerned with the problem of sampled-data H∞filtering for delayed neural network subject to sensor saturation. Rather than the continuous measurements, the neural network measurements are sampled and then transmitted to the filter simultaneously considering sensor saturation constraints. By using the input delay approach,the sampling period is converted into a bounded time-vary delay in the filter error system. Sensor saturation function is decomposed into a linear part and a dead-zone nonlinear remainder term. By utilizing Lyapunov-functional approach,sufficient conditions have been obtained to ensure that the filter error dynamics is exponentially stable. Finally, an example is provided to illustrate the superiority and usefulness of the proposed filter approach.
This paper is concerned with the problem of sampled-data H_∞filtering for delayed neural network subject to sensor saturation. Rather than the continuous measurements, the neural network measurements are sampled and then transmitted to the filter simultaneously considering sensor saturation constraints. By using the input delay approach,the sampling period is converted into a bounded time-vary delay in the filter error system. Sensor saturation function is decomposed into a linear part and a dead-zone nonlinear remainder term. By utilizing Lyapunov-functional approach,sufficient conditions have been obtained to ensure that the filter error dynamics is exponentially stable. Finally, an example is provided to illustrate the superiority and usefulness of the proposed filter approach.
This paper is concerned with the problem of sampled-data H_∞filtering for delayed neural network subject to sensor saturation. Rather than the continuous measurements, the neural network measurements are sampled and then transmitted to the filter simultaneously considering sensor saturation constraints. By using the input delay approach,the sampling period is converted into a bounded time-vary delay in the filter error system. Sensor saturation function is decomposed into a linear part and a dead-zone nonlinear remainder term. By utilizing Lyapunov-functional approach,sufficient conditions have been obtained to ensure that the filter error dynamics is exponentially stable. Finally, an example is provided to illustrate the superiority and usefulness of the proposed filter approach.
引文
[1]S.Haykin,Neural networks:a comprehensive foundation,Tsinghua University Press,2001.
[2]H.Haken,Pattern recognition and synchronization in pulsecoupled neural networks,Nonlinear Dynamics,44(1-4):269-276,2006.
[3]A.Rawat,R.N.Yadav,S.C.Shrivastava,Neural network applications in smart antenna arrays:a review,AEUInternational Journal of Electronics and Communications,66(11):903C912,2012.
[4]H.Wang,P.Shi,J.Zhang,Event-triggered fuzzy filtering for a class of nonlinear networked control systems,Signal Processing,113:159-168,2015.
[5]J.Liu,J.Tang,S.Fei,Event-triggered H∞filter design for delayed neural network with quantization,Neural Networks,82:39-48,2016.
[6]M.S.Ali,R.Saravanakumar,C.K.Ahn,H.R.Karimi,Stochastic H∞filtering for neural networks with leakage delay and mixed time-varying delays,Information Sciences,388-389:118-134,2017.
[7]M.S.Ali,R.Saravanakumar,S.Arik,Novel H∞state estimation of static neural networks with interval time-varying delays via augmented Lyapunov CKrasovskii functional,Neurocomputing,171:949-954,2016.
[8]J.Liu,S.Fei,E.Tian,Z.Gu,Co-design of event generator and filtering for a class of T-S fuzzy systems with stochastic sensor faults,Fuzzy Sets and Systems,273:124C140,2015.
[9]T.W.Chen,B.Francis,Optimal Sample-Data Control Systems,New York:Springer,1995
[10]E.Fridman,Use of models with aftereffect in the problem of the design of optimal digital-control systems,Automation and Remote Control,53(10):1523-1528,1992.
[11]Y.V.Mikheev,V.A.Sobolev,E.Fridman,Asymptotic analysis of digital-control systems,Automation and Remote Control,49(9):1175-1180,1988.
[12]R.Sakthivel,S.A.Karthick,B.Kaviarasan,Reliable state estimation of switched neutral system with nonlinear actuator faults via sampled-data control,Applied Mathematics and Computation,311:129-147,2017.
[13]B.Wang,J.Cheng,A.Al-Barakati,M.F.Habib,A mismatched membership function approach to sampled-data stabilization for T-S fuzzy systems with time-varying delayed signals,Signal Processing,140:161-170,2017.
[14]X.Liu,W.Yu,J.Cao,S.Chen,Discontinuous Lyapunov approach to state estimation and filtering of jumped systems with sampled-data,Neural Networks,68:12-22,2015.
[15]Z.Wang,B.Shen,X.Liu,H∞filtering with randomly occurring sensor saturations and missing measurements,Automatica,48(3):556-562,2012.
[16]F.Yang,Y.Li,Set-membership filtering for systems with sensor saturation,Automatica,45(8):1896-1902,2009.
[17]W.Yang W,M.Liu,P.Shi,H∞filtering for nonlinear stochastic systems with sensor saturation,quantization and random packet losses,Signal Processing,92(6):1387-1396,2012.
[2]H.Haken,Pattern recognition and synchronization in pulsecoupled neural networks,Nonlinear Dynamics,44(1-4):269-276,2006.
[3]A.Rawat,R.N.Yadav,S.C.Shrivastava,Neural network applications in smart antenna arrays:a review,AEUInternational Journal of Electronics and Communications,66(11):903C912,2012.
[4]H.Wang,P.Shi,J.Zhang,Event-triggered fuzzy filtering for a class of nonlinear networked control systems,Signal Processing,113:159-168,2015.
[5]J.Liu,J.Tang,S.Fei,Event-triggered H∞filter design for delayed neural network with quantization,Neural Networks,82:39-48,2016.
[6]M.S.Ali,R.Saravanakumar,C.K.Ahn,H.R.Karimi,Stochastic H∞filtering for neural networks with leakage delay and mixed time-varying delays,Information Sciences,388-389:118-134,2017.
[7]M.S.Ali,R.Saravanakumar,S.Arik,Novel H∞state estimation of static neural networks with interval time-varying delays via augmented Lyapunov CKrasovskii functional,Neurocomputing,171:949-954,2016.
[8]J.Liu,S.Fei,E.Tian,Z.Gu,Co-design of event generator and filtering for a class of T-S fuzzy systems with stochastic sensor faults,Fuzzy Sets and Systems,273:124C140,2015.
[9]T.W.Chen,B.Francis,Optimal Sample-Data Control Systems,New York:Springer,1995
[10]E.Fridman,Use of models with aftereffect in the problem of the design of optimal digital-control systems,Automation and Remote Control,53(10):1523-1528,1992.
[11]Y.V.Mikheev,V.A.Sobolev,E.Fridman,Asymptotic analysis of digital-control systems,Automation and Remote Control,49(9):1175-1180,1988.
[12]R.Sakthivel,S.A.Karthick,B.Kaviarasan,Reliable state estimation of switched neutral system with nonlinear actuator faults via sampled-data control,Applied Mathematics and Computation,311:129-147,2017.
[13]B.Wang,J.Cheng,A.Al-Barakati,M.F.Habib,A mismatched membership function approach to sampled-data stabilization for T-S fuzzy systems with time-varying delayed signals,Signal Processing,140:161-170,2017.
[14]X.Liu,W.Yu,J.Cao,S.Chen,Discontinuous Lyapunov approach to state estimation and filtering of jumped systems with sampled-data,Neural Networks,68:12-22,2015.
[15]Z.Wang,B.Shen,X.Liu,H∞filtering with randomly occurring sensor saturations and missing measurements,Automatica,48(3):556-562,2012.
[16]F.Yang,Y.Li,Set-membership filtering for systems with sensor saturation,Automatica,45(8):1896-1902,2009.
[17]W.Yang W,M.Liu,P.Shi,H∞filtering for nonlinear stochastic systems with sensor saturation,quantization and random packet losses,Signal Processing,92(6):1387-1396,2012.