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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 3/2017

24.10.2015 | Original Article

Sampled-data synchronization of randomly coupled reaction–diffusion neural networks with Markovian jumping and mixed delays using multiple integral approach

verfasst von: R. Rakkiyappan, S. Dharani

Erschienen in: Neural Computing and Applications | Ausgabe 3/2017

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Abstract

This paper is devoted to investigate the problem of global asymptotic synchronization of an array of N randomly coupled reaction–diffusion neural networks with Markovian jumping parameters and mixed delays using sampled-data control technique. The jump parameters are determined by a continuous-time, discrete-state Markovian chain, and the mixed time delays under consideration comprise both discrete and distributed delays. A multiple integral inequality is proposed firstly in Markovian jump reaction–diffusion neural networks with mixed delays. Through constructing appropriate Lyapunov–Krasovskii functional including multiple integral terms, some novel synchronization criteria in terms of linear matrix inequalities are derived. The obtained LMIs can be easily verified for feasibility through any of the available softwares. Finally, numerical examples with simulations are provided to illustrate the effectiveness of the proposed theoretical results.
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Literatur
1.
Young S, Scott P, Nasrabadi N (1997) Object recognition using multilayer Hopfield neural network. IEEE Trans Image Process 6:357–372CrossRef
2.
Atencia M, Joya G, Sandoval F (2005) Dynamical analysis of continuous higher order Hopfield networks for combinatorial optimization. Neural Comput 17:1802–1819MathSciNetCrossRefMATH
3.
Sheikhan M, Shahnazi R, Garoucy S (2013) Synchronization of general chaotic systems using neural controllers with application to secure communication. Neural Comput Appl 22:361–373CrossRef
4.
Huang H, Feng G (2009) Synchronization of nonidentical chaotic neural networks with time delays. Neural Netw 22:1841–1845CrossRefMATH
5.
Li X, Ding C, Zhu Q (2010) Synchronization of stochastic perturbed chaotic neural networks with mixed delays. J Franklin Inst 347:1266–1280MathSciNetCrossRefMATH
6.
Liang J, Wang Z, Li P (2009) Robust Synchronization of delayed neural networks with both linear and non-linear couplings. Int J Syst Sci 40:973–984CrossRefMATH
7.
Grassi G, Mascolo S (1999) Synchronizing high dimensional chaotic systems via eigenvalue placement in application to cellular neural networks. Int J Bifurc Chaos 9:705–711CrossRefMATH
8.
Zhang L (2009) \(H_\infty\) estimation for discrete-time piecewise homogeneous Markov jump linear systems. Automatica 45:2570–2576MathSciNetCrossRefMATH
9.
Zhang W, Li C, Huang T, Qi J (2014) Global exponential synchronization for coupled switched delayed recurrent neural networks with stochastic perturbation and impulsive effects. Neural Comput Appl 25:1275–1283CrossRef
10.
Cao J, Wan Y (2014) Matrix measure strategies for stability and synchronization for inertial BAM neural network with time delay. Neural Netw 53:165–172CrossRefMATH
11.
12.
Gong D, Liu J, Zhou S (2015) Chaotic synchronization for coupled neural networks based on T-S fuzzy theory. Int J Syst Sci 46:681–689CrossRefMATH
13.
Liu X (2010) Synchronization of linearly coupled neural networks with reaction–diffusion terms and unbounded time delays. Neurocomputing 73:2681–2888CrossRef
14.
Wang K, Teng Z, Jiang H (2012) Adaptive synchronization in an array of linearly coupled neural networks with reaction–diffusion terms and time delays. Commun Nonlinear Sci Numer Simul 17:3866–3875MathSciNetCrossRefMATH
15.
Yang X, Cao J, Yang Z (2013) Synchronization of coupled reaction–diffusion neural networks with time-varying delays via pinning-impulsive controller. SIAM J Control Optim 51:3486–3510MathSciNetCrossRefMATH
16.
Hu C, Jiang H, Teng Z (2010) Impulsive control and synchronization for delayed neural networks with reaction–diffusion terms. IEEE Trans Neural Netw 21:67–81CrossRef
17.
Liu Z, Peng J (2010) Delay-independent stability of stochastic reaction–diffusion neural networks with Dirichlet boundary conditions. Neural Comput Appl 19:151–158CrossRef
18.
Wang Y, Cao J (2007) Synchronization of a class of delayed neural networks with reaction–diffusion terms. Phys Lett A 369:201–211CrossRef
19.
Krasovskii NN, Lidskii EA (1961) Analysis and design of controllers in systems with random attributes. Autom Remote Control 22:1021–1025
20.
Liu Y, Wang Z, Liu X (2008) Exponential synchronization of complex networks with Markovian jump and mixed delays. Phys Lett A 372:3986–3998MathSciNetCrossRefMATH
21.
Shen H, Xu S, Zhou J, Lu J (2011) Fuzzy \(H_\infty\) filtering for nonlinear Markovian jump neutral systems. Int J Syst Sci 42:767–780MathSciNetCrossRefMATH
22.
Wang Z, Liu L, Liu X (2010) Exponential stability of a class of stochastic system with Markovian jump parameters and mode-dependent mixed time-delays. IEEE Trans Autom Control 55:1656–1662CrossRef
23.
Chen B, Huang J, Niu Y (2012) Sliding mode control for Markovian jumping systems with actuator nonlinearities. Int J Syst Sci 43:656–664MathSciNetCrossRefMATH
24.
Xu S, Chen T, Lam J (2003) Robust \(H_\infty\) filtering for uncertain Markovian jump systems with mode-dependent time delays. IEEE Trans Autom Control 48:900–908CrossRef
25.
Liu Y (2009) Stochastic asymptotic stability of Markovian jumping neural networks with Markov mode estimation and mode-dependent delays. Phys Lett A 373:3741–3742MathSciNetCrossRefMATH
26.
Zhang L, Boukas E, Lam J (2008) Analysis and synthesis of Markov jump linear systems with time-varying delays and partially known transition probabilities. IEEE Trans Autom Control 53:2458–2464MathSciNetCrossRef
27.
Balasubramaniam P, Vembarasan V, Rakkiyappan R (1999) Delay-dependent robust exponential state estimation of Markovian jumping fuzzy Hopfield neural networks with mixed random time-varying. IEEE Trans Circuits Syst I Fundam Theory Appl 46:1144–1150CrossRefMATH
28.
Wang L, Zhang Z, Wang Y (2008) Stochastic exponential stability of the delayed reaction–diffusion recurrent neural networks with Markovian jumping parameters. Phys Lett A 372:3201–3209MathSciNetCrossRefMATH
29.
Zhang H, Wang J, Wang Z, Liang H Mode-dependent stochastic synchronization for Markovian coupled neural networks with time-varying mode-delays. IEEE Trans Neural Netw Learn Syst. doi:10.​1109/​TNNLS/​2014.​2387885
30.
Zheng CD, Zhang H, Wang Z (2014) Exponential synchronization of stochastic chaotic neural networks with mixed time delays and Markovian switching. Neural Comput Appl 25:429–442CrossRef
31.
Yang X, Cao J, Lu J (2013) Synchronization of randomly coupled neural networks with Markovian jumping and time-delay. IEEE Trans Circuits Syst I Regul Pap 60:363–376MathSciNetCrossRef
32.
Kao YG, Guo JF, Wang CH, Sun XQ (2012) Delay-dependent robust exponential stability of Markovian jumping reaction–diffusion Cohen-Grossberg neural networks with mixed delays. J Franklin Inst 349:1972–1988MathSciNetCrossRefMATH
33.
Zhang L, Zhu YZ, Zheng WX Synchronization and state estimation of a class of hierarchical hybrid neural networks with time-varying delays. IEEE Trans Neural Netw Learn Syst. doi:10.​1109/​TNNLS.​2015.​2412676
34.
Shi G, Ma Q (2011) Synchronization of stochastic Markovian jump neural networks with reaction–diffusion terms. Neurocomputing 77:275–280CrossRef
35.
Zhou J, Shen H, Wang Z (2012) Synchronization of delayed reaction–diffusion neural networks with markovian jumping parameters. In: The second international conference on computer application and system modeling
36.
Wang X, Li C, Huang T, Chen L (2014) Impulsive exponential synchronization of randomly coupled neural networks with Markovian jumping and mixed model-dependent time delays. Neural Netw 60:25–32CrossRefMATH
37.
Zhang CK, He Y, Wu M (2010) Exponential synchronization of neural networks with time-varying mixed delays and sampled-data. Neurocomputing 74:265–273CrossRef
38.
Li N, Zhang Y, Hu J, Nie Z (2011) Synchronization for general complex dynamical networks with sampled-data. Neurocomputing 74:805–811CrossRef
39.
Liu YY, Yang GH (2012) Sampled-data \(H_\infty\) control for networked control systems with digital control inputs. Int J Syst Sci 43:1728–1740MathSciNetCrossRefMATH
40.
Li L, Yang Y, Lin G The stabilization of BAM neural networks with time-varying delays in the leakage terms via sampled-data control. Neural Comput Appl. doi:10.​1007/​s00521-015-1865-4
41.
Kalpana M, Balasubramaniam P (2013) Stochastic asymptotical synchronization of chaotic Markovian jumping fuzzy cellular neural networks with mixed delays and the Wiener process based on sampled-data control. Chin Phys B 22:078401CrossRef
42.
Zhang CK, He Y, Wu M (2009) Improved global asymptotical synchronization of chaotic Lur’e systems with sampled-data control. IEEE Trans Circuits Syst II Express Briefs 56:320–324CrossRef
43.
Wu Z, Shi P, Su H, Chu J (2013) Stochastic synchronization of Markovian jump neural networks with time-varying delay using sampled-data. IEEE Trans Cybern 43:1796–1806CrossRef
44.
Astrom K, Wittenmark B (1989) Adaptive control. Addison-Wesley, Reading
45.
Mikheev Y, Sobolev V, Fridman E (1988) Asymptotic analysis of digital control systems. Autom Remote Control 49:1175–1180MathSciNetMATH
46.
Fang M, Park JH (2013) A multiple integral approach to stability of neutral time-delay systems. Appl Math Comput 224:714–718MathSciNetMATH
47.
Wang J, Zhang H, Wang Z, Wang B (2013) Local exponential synchronization in complex dynamical networks with time-varying delay and hybrid coupling. Appl Math Comput 225:16–32MathSciNetMATH
48.
Park JH, Lee TH (2015) Synchronization of complex dynamical networks with discontinuous coupling signals. Nonlinear Dyn 79:1353–1362MathSciNetCrossRefMATH
49.
Fridman E, Seuret A, Richard JP (2004) Robust sampled-data stabilization of linear systems: an input delay approach. Automatica 40:1441–1446MathSciNetCrossRefMATH
50.
Lu JG (2008) Global exponential stability and periodicity of reaction–diffusion delayed recurrent neural networks with Dirichlet boundary conditions. Chaos Solitons Fractals 35:116–125MathSciNetCrossRefMATH
51.
Zhang HG, Ma TD, Huang GB, Wang CX (2010) Robust global exponential synchronization of uncertain chaotic delayed neural networks via dual-stage impulsive control. IEEE Trans Syst Man Cybern B Cybern 40:831–844CrossRef
52.
Yu W, Cao J, Chen G, Lu J, Han J, Wei W (2009) Local synchronization of a complex network model. IEEE Trans Syst Man Cybern B Cybern 39:230–241CrossRef
53.
Chen J, Chen X (2001) Special matrices. Tsinghua University Press, Beijing
54.
Tahara S, Fujii T, Yokoyama T (2007) Variable sampling quasi multirate deadbeat control method for single phase PWM inverter in low carrier frequency. In: Power conversion conference (PCC), pp 804–809
55.
Hu B, Michel AN (2000) Stability analysis of digital feedback control systems with time-varying sampling periods. Automatica 36:897–905MathSciNetCrossRefMATH
56.
57.
58.
Lee T, Park J, Lee S, Kwon O (2013) Robust synchronization of chaotic systems with randomly occurring uncertainties via stochastic sampled-data control. Int J Control 86:107–119CrossRefMATH
59.
Lee T, Park J, Kwon O, Lee S (2013) Stochastic sampled-data control for state estimation of time-varying delayed neural networks. Neural Netw 46:99–108CrossRefMATH
60.
Ding S, Wang Z (2015) Stochastic exponential synchronization control of Memristive neural networks with multiple time-varying delays. Neurocomputing 162:16–25CrossRef
61.
Wang Z, Zhang H, Jiang B (2011) LMI-based approach for global asymptotic stability analysis of recurrent neural networks with various delays and structures. IEEE Trans Neural Netw 22:1032–1045CrossRef
62.
Wang Z, Liu L, Shan Q-H, Zhang H Stability criteria for recurrent neural networks with time-varying delay based on secondary delay partitioning method. IEEE Trans Neural Netw Learn Syst. doi:10.​1109/​TNNLS.​2014.​2387434
Metadaten
Titel
Sampled-data synchronization of randomly coupled reaction–diffusion neural networks with Markovian jumping and mixed delays using multiple integral approach
verfasst von
R. Rakkiyappan
S. Dharani
Publikationsdatum
24.10.2015
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 3/2017
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-015-2079-5

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