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Linear matrix inequality approach for synchronization of chaotic fuzzy cellular neural networks with discrete and unbounded distributed delays based on\ sampled-data control | ||
Iranian Journal of Fuzzy Systems | ||
مقاله 4، دوره 12، شماره 5، دی 2015، صفحه 77-98 اصل مقاله (1020.38 K) | ||
نوع مقاله: Research Paper | ||
شناسه دیجیتال (DOI): 10.22111/ijfs.2015.2112 | ||
نویسندگان | ||
P. Balasubramaniam-pour* 1؛ K. Ratnavelu2؛ M. Kalpana2 | ||
1Department of Mathematics, Gandhigram Rural Institute - Deemed University, Gandhigram - 624 302, Tamilnadu, India | ||
2Institute of Mathematical Sciences, Faculty of Science, University of Malaya - 50603, Kuala Lumpur, Malaysia | ||
چکیده | ||
In this paper, linear matrix inequality (LMI) approach for synchronization of chaotic fuzzy cellular neural networks (FCNNs) with discrete and unbounded distributed delays based on sampled-data control is investigated. Lyapunov-Krasovskii functional combining with the input delay approach as well as the free-weighting matrix approach are employed to derive several sufficient criteria in terms of LMIs ensuring the delayed FCNNs to be asymptotically synchronous. The restriction such as the time-varying delay required to be differentiable or even its time-derivative assumed to be smaller than one, are removed. Instead, the time-varying delay is only assumed to be bounded. Finally, numerical examples and its simulations are provided to demonstrate the effectiveness of the derived results. | ||
کلیدواژهها | ||
Chaos؛ Fuzzy cellular neural networks؛ Linear matrix inequality؛ Sampled-data control؛ Synchronization | ||
مراجع | ||
[1] A. Arunkumar, R. Sakthivel, K. Mathiyalagan and S. Marshal Anthoni, Robust state estimation for discrete-time BAM neural networks with time-varying delay, Neurocomputing, 131 (2014), 171-178. [2] A. Arunkumar, R. Sakthivel, K. Mathiyalagan and Ju H. Park, Robust stochastic stability of discrete-time fuzzy Markovian jump neural networks, ISA Transactions, 53 (2014), 1006– 1014. [3] P. Balasubramaniam, M. Kalpana and R. Rakkiyappan, Linear matrix inequality approach for synchronization control of fuzzy cellular neural networks with mixed time delays, Chinese Physics B, 21 (2012): 048402. [4] S. Boyd, L. E. Ghaoui, E. Feron and V. Balakrishnan, Linear Matrix Inequalities in Systems and Control Theory (SIAM, Philadelphia, 1994). [5] T. L. Carroll and L. M. Pecora, Synchronization chaotic circuits, IEEE Trans. Circuits Syst., 38 (1991), 453–456. [6] L. O. Chua and L. Yang, Cellular neural networks: theory, IEEE Trans. Circuits Syst., 35 (1988), 1257-1272. [7] L. O. Chua and L. Yang, Cellular neural networks: applications, IEEE Trans. Circuits Syst., 35 (1988), 1273-1290. [8] X. Feng, F. Zhang and W. Wang, Global exponential synchronization of delayed fuzzy cellular neural networks with impulsive effects, Chaos Solitons Fractals., 44 (2011), 9–16. [9] T. Feuring, J. J. Buckley, W. M. Lippe and A. Tenhagen, Stability analysis of neural net controllers using fuzzy neural networks, Fuzzy Sets and Systems, 101 (1999), 303–313. [10] E. Fridman, A. Seuret and J. P. Richard, Robust sampled-data stabilization of linear systems: an input delay approach, Automatica, 40 (2004), 1441–1446. [11] Q. Gan and Y. Liang, Synchronization of chaotic neural networks with time delay in the leakage term and parametric uncertainties based on sampled-data control, J. Franklin Inst., 349 (2012), 1955–1971. [12] Q. Gan, R. Xu and P. Yang, Synchronization of non-identical chaotic delayed fuzzy cellular neural networks based on sliding mode control, Commun. Nonlinear Sci. Numer. Simul., 17 (2012), 433–443. [13] Q. Gan, R. Xu and P. Yang, Exponential synchronization of stochastic fuzzy cellular neural networks with time delay in the leakage term and reaction-diffusion, Commun. Nonlinear Sci. Numer. Simul., 17 (2012), 1862–1870. [14] K. Gu, An integral inequality in the stability problem of time-delay systems, in Proceedings of the 39th IEEE Conference on Decision and Control Sydney, Australia (2000), 2805–2810. [15] S. Lee, V. Kapila, M. Porfiri and A. Panda, Master-slave synchronization of continuously and intermittently coupled sampled-data chaotic oscillators, Commun. Nonlinear Sci. Numer. Simul., 15 (2010), 4100–4113. [16] T. Li, S. Fei and Q. Zhu, Design of exponential state estimator for neural networks with distributed delays, Nonlinear Anal. Real World Appl., 10 (2009), 1229–1242. [17] N. Li, Y. Zhang, J. Hu and Z. Nie, Synchronization for general complex dynamical networks with sampled-data, Neurocomputing, 74 (2011), 805–811. [18] Z. Liu, H. Zhang and Z. Wang, Novel stability criterions of a new fuzzy cellular neural networks with time-varying delays, Neurocomputing, 72 (2009), 1056–1064. [19] J. Lu and D. J. Hill, Global asymptotical synchronization of chaotic Lur’e systems using sampled data: a linear matrix inequality approach, IEEE Trans. Circuits Syst. II, 55 (2008), 586–590. [20] K. Mathiyalagan, S. Hongye and R. Sakthivel, Robust stochastic stability of discrete-time Markovian jump neural networks with leakage delay, Zeitschrift Fur Naturforschung Section A-A Journal of Physical Sciences, 69 (2014), 70–80. [21] K. Mathiyalagan, R. Sakthivel and S. Hongye, Exponential state estimation for discretetime switched genetic regulatory networks with random delays, Canad. J. Phys., 92 (2014), 976–986. [22] L. M. Pecora and T. L. Carroll, Synchronization in chaotic systems, Phys. Rev. Lett., 64 (1990), 821–824. [23] L. M. Pecora, T. L. Carroll, G. A. Johnson, D. J. Mar and J. F. Heagy, Fundamentals of synchronization in chaotic systems, concepts, and applications, Chaos, 7 (1997), 520–543. [24] Y. Ping and L. Teng, Exponential synchronization of fuzzy cellular neural networks with mixed delays and general boundary conditions, Commun. Nonlinear Sci. Numer. Simul., 17 (2012), 1003–1011. [25] R. Sakthivel, R. Raja and S. Marshal Anthoni, Linear matrix inequality approach to stochastic stability of uncertain delayed BAM neural networks, IMA J Appl Math., 78 (2013), 1156– 1178. [26] E. N. Sanchez and J. P. Perez, Input-to-state stability (ISS) analysis for dynamic neural networks, IEEE Trans. Circuits Syst. I, 46 (1999), 1395-1398. [27] F. O. Souza, R. M. Palhares and P. Y. Ekel, Asymptotic stability analysis in uncertain multidelayed state neural networks via Lyapunov-Krasovskii theory, Math. Comput. Modelling, 45 (2007), 1350–1362. [28] F. O. Souza, R. M. Palhares and P. Y. Ekel, Novel stability criteria for uncertain delayed Cohen-Grossberg neural networks using discretized Lyapunov functional, Chaos Solitons Fractals, 41 (2009), 2387–2393. [29] F. O. Souza, R. M. Palhares and P. Y. Ekel, Improved asymptotic stability analysis for uncertain delayed state neural networks, Chaos Solitons Fractals, 39 (2009), 240–247. [30] Y. Tang and J. Fang, Robust synchronization in an array of fuzzy delayed cellular neural networks with stochastically hybrid coupling, Neurocomputing, 72 (2009), 3253–3262. [31] T. Yang and L. B. Yang, Global stability of fuzzy cellular neural network, IEEE Trans. Circuits Syst. I, 43 (1996), 880–883. [32] T. Yang, L. B. Yang, C. W. Wu and L. O. Chua, Fuzzy cellular neural networks: Theory, in Proceedings of the IEEE International Workshop on Cellular Neural Networks and Applications, (1996), 181–186. [33] T. Yang, L. B. Yang, C. W. Wu and L. O. Chua, Fuzzy cellular neural networks: Applications, in Proceedings of the IEEE International Workshop on Cellular Neural Networks and Applications, (1996), 225–230. [34] J. Yu, C. Hu, H. Jiang and Z. Teng, Exponential lag synchronization for delayed fuzzy cellular neural networks via periodically intermittent control, Math. Comput. Simulation, 82 (2012), 895–908. [35] F. Yu and H. Jiang, Global exponential synchronization of fuzzy cellular neural networks with delays and reaction-diffusion terms, Neurocomputing, 74 (2011), 509–515. [36] L. A. Zadeh, Fuzzy Sets, Information and Control, 8 (1965), 338–353. [37] C. Zhang, Y. He and M. Wu, Exponential synchronization of neural networks with timevarying mixed delays and sampled-data, Neurocomputing, 74 (2010), 265–273. | ||
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