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Free-matrix-based time-dependent discontinuous Lyapunov functional for synchronization of delayed neural networks with sampled-data control |
| Wei Wang(王炜)1,3, Hong-Bing Zeng(曾红兵)1,3, Kok-Lay Teo2 |
1. School of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou 412007, China;
2. Department of Mathematics and Statistics, Curtin University, Perth, WA 6102, Australia;
3. Key Laboratory for Electric Drive Control and Intelligent Equipment of Hunan Province, Zhuzhou 412007, China |
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Abstract This paper is concerned with the synchronization of delayed neural networks via sampled-data control. A new technique, namely, the free-matrix-based time-dependent discontinuous Lyapunov functional approach, is adopted in constructing the Lyapunov functional, which takes advantage of the sampling characteristic of sawtooth input delay. Based on this discontinuous Lyapunov functional, some less conservative synchronization criteria are established to ensure that the slave system is synchronous with the master system. The desired sampled-data controller can be obtained through the use of the linear matrix inequality (LMI) technique. Finally, two numerical examples are provided to demonstrate the effectiveness and the improvements of the proposed methods.
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Received: 11 April 2017
Revised: 25 July 2017
Accepted manuscript online:
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PACS:
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05.45.Gg
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(Control of chaos, applications of chaos)
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07.05.Mh
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(Neural networks, fuzzy logic, artificial intelligence)
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| Fund: Project supported by the National Natural Science Foundation of China (Grant No. 61304064), the Scientific Research Fund of Hunan Provincial Education Department, China (Grant Nos. 15B067 and 16C0475), and a Discovering Grant from Australian Research Council. |
Corresponding Authors:
Hong-Bing Zeng
E-mail: 9804zhb@163.com
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Cite this article:
Wei Wang(王炜), Hong-Bing Zeng(曾红兵), Kok-Lay Teo Free-matrix-based time-dependent discontinuous Lyapunov functional for synchronization of delayed neural networks with sampled-data control 2017 Chin. Phys. B 26 110503
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| [1] |
Pecora L M and Carroll T L 1990 Phys. Rev. Lett. 64 821
|
| [2] |
Wu Z G, Shi P, Su H and Chu J 2013 IEEE Transactions on Neural Networks and Learning Systems 24 1177
|
| [3] |
Zhang C K, Jiang L, He Y, Wu Q H and Wu M 2013 Commun. Nonlinear Sci. Numer. Simulat. 18 2743
|
| [4] |
Ma D Z, Li X U, Sun Q Y and Zhang H G 2016 Acta Phys. Sin. 65 200501(in Chinese)
|
| [5] |
Zeng H B, Park J H, Xiao S P and Liu Y 2015 Nonlinear Dynamics 82 851
|
| [6] |
Leung Y T A, Li X F, Chu Y D and Zhang H 2015 Chin. Phys. B 24 0100502
|
| [7] |
Wu Z G, Shi P, Su H and Chu J 2013 IEEE Transactions on Cybernetics 43 1796
|
| [8] |
Zeng H B, He Y, Wu M and Xiao H 2014 IEEE Transactions on Cybernetics 44 785
|
| [9] |
Zeng H B, Park J H, Zhang C F and Wang W 2015 Journal of the Franklin Institute 352 1284
|
| [10] |
He Y, Liu G and Rees D 2007 IEEE Transactions on Neural Networks 18 310
|
| [11] |
Li T, Guo L, Sun C and Lin C 2008 IEEE Transactions on Neural Networks 19 726
|
| [12] |
Zhang C K, He Y, Jiang L, Wu Q H and Wu M 2014 IEEE Transactions on Neural Networks and Learning Systems 25 1263
|
| [13] |
Zhang X M and Han Q L 2011 IEEE Transactions on Neural Networks and Learning Systems 22 1180
|
| [14] |
Zhang X M and Han Q L 2014 Neural Networks 54 57
|
| [15] |
Zeng H B, He Y, Wu M and Xiao H 2015 Neurocomputing 161 148
|
| [16] |
Liu Y, Wang Z and Liu X 2006 Neural Networks 19 667
|
| [17] |
He Y, Ji M D, Zhang C K and Wu M 2016 Neural Networks 77 80
|
| [18] |
Zeng H B, Park J H and Shen H 2015 Neurocomputing 149 1092
|
| [19] |
Zhang C K, He Y, Jiang L, Lin W J and Wu M 2017 Applied Mathematics and Computation 294 102
|
| [20] |
Park J H and Kwon O M 2009 Chaos Solitons& Fractals 42 1299
|
| [21] |
Zeng H B, Teo K L, He Y, Xu H and Wang W 2017 Neurocomputing 260 25
|
| [22] |
Li P, Cao J and Wang Z 2007 Physica A 373 261
|
| [23] |
Liu H, Li S G, Wang H X and Li G J X 2017 Chin. Phys. B 26 030504
|
| [24] |
Liu H, Yu H J and Xiang W 2012 Chin. Phys. B 21 120505
|
| [25] |
Yu W and Cao J 2007 Physica A 373 252
|
| [26] |
Karimi H R and Maass P 2009 Chaos, Solitons& Fractals 41 1125
|
| [27] |
Li T, Wang T, Zhang G and Fei S 2016 Neurocomputing 205 498
|
| [28] |
Zeng H B, He Y, Wu M and She J 2015 Automatica 60 189
|
| [29] |
Zeng H B, He Y, Wu M and She J 2015 IEEE Transactions on Automatic Control 60 2768
|
| [30] |
Han X, Wu H and Fang B 2016 Neurocomputing 201 40
|
| [31] |
Bao H, Park J H and Cao J 2016 Neural Networks 81 16
|
| [32] |
Qi D, Liu M, Qiu M and Zhang S 2010 IEEE Transactions on Neural Networks 21 1358
|
| [33] |
Mu X and Chen Y 2016 Neurocomputing 175 293
|
| [34] |
Liu K and Fridman E 2012 Automatica 48 102
|
| [35] |
Gao H, Chen T and Lam J 2008 Automatica 44 39
|
| [36] |
Fridman E 2010 Automatica 46 421
|
| [37] |
Zeng H B, Teo K L and He Y 2017 Automatica 82 328
|
| [38] |
Zhang C, He Y and Wu M 2010 Neurocomputing 74 265
|
| [39] |
Wu Z G, Park J H, Su H and Chu J 2012 Nonlinear Dynamics 69 2021
|
| [40] |
Park P G, Ko J W and Jeong C 2011 Automatica 47 235
|
| [41] |
Seuret A and Gouaisbaut F 2013 Automatica 49 2860
|
| [42] |
Lee T H and Park J H 2017 IEEE Transactions on Automatic Control
|
| [43] |
Lee T H and Park J H 2017 Nonlinear Analysis:Hybrid Systems 24 132
|
| [44] |
Lee T H and Park J H 2017 IEEE Transactions on Systems, Man, and Cybernetics:Systems
|
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