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Stability and Existence of Periodic Solutions for Cellular Neural Networks with State Dependent Delays on Time Scales

DOI: 10.1155/2012/386706

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Abstract:

We study delayed cellular neural networks on time scales. Without assuming the boundedness of the activation functions, we establish the exponential stability and existence of periodic solutions. The results in this paper are completely new even in case of the time scale or and improve some of the previously known results. 1. Introduction Consider the following cellular neural networks with state-dependent delays on time scales: where , is an -periodic time scale which has the subspace topology inherited from the standard topology on , , will be defined in the next section, , corresponds to the number of units in the neural network, corresponds to the state of the th unit at time , denotes the output of the th unit on th unit at time , denotes the strength of the th unit on the th unit at time , denotes the external bias on the th unit at time , corresponds to the transmission delay along the axon of the th unit, represents the rate with which the th unit will reset its potential to the resting state in isolation when disconnected from the network and external inputs. It is well known that the cellular neural networks have been successfully applied to signal processing, pattern recognition, optimization, and associative memories, especially in image processing and solving nonlinear algebraic equations. They have been widely studied both in theory and applications [1–3]. Many results for the existence of their periodic solutions and the exponential convergence properties for cellular neural networks have been reported in the literatures. See, for instance, [4–17] and references cited therein. In fact, continuous and discrete systems are very important in implementation and applications. It is well known that the theory of time scales has received a lot of attention which was introduced by Stefan Hilger in order to unify continuous and discrete analysis. Therefore, it is meaningful to study dynamic systems on time scales which can unify differential and difference systems see [18–28]. When , , (1.1) reduces to where . By using Mawhin’s continuation theorem and Liapunov functions, the authors [6, 14] obtained the existence and stability of periodic solutions of (1.2), respectively. Furthermore, (1.1) also covers discrete system (for when ; see [15]) where , . In [15], the author firstly obtained the discrete-time analogue of (1.3) by the semidiscretization technique [29, 30], and then some sufficient conditions for the existence and global asymptotical stability of periodic solutions of (1.3) were established by using Mawhin’s continuation theorem and

References

[1]  L. O. Chua, CNN: A Paradigm for Complexity, World Scientific, Singapore, 1998.
[2]  L. O. Chua and L. Yang, “Cellular neural networks: applications,” Institute of Electrical and Electronics Engineers, vol. 35, no. 10, pp. 1273–1290, 1988.
[3]  T. Roska and J. Vandewalle, Cellular Neural Networks, Wiley, NewYork, NY, USA, 1995.
[4]  D. Zhou and J. Cao, “Globally exponential stability conditions for cellular neural networks with time-varying delays,” Applied Mathematics and Computation, vol. 131, no. 2-3, pp. 487–496, 2002.
[5]  J. Cao, “New results concerning exponential stability and periodic solutions of delayed cellular neural networks,” Physics Letters A, vol. 307, no. 2-3, pp. 136–147, 2003.
[6]  Y. Li, L. Zhu, and P. Liu, “Existence and stability of periodic solutions of delayed cellular neural networks,” Nonlinear Analysis: Real World Applications, vol. 7, no. 2, pp. 225–234, 2006.
[7]  Y. Li and Z. Xing, “Existence and global exponential stability of periodic solution of CNNs with impulses,” Chaos, Solitons & Fractals, vol. 33, no. 5, pp. 1686–1693, 2007.
[8]  H. Liu and L. Wang, “Globally exponential stability and periodic solutions of CNNs with variable coefficients and variable delays,” Chaos, Solitons and Fractals, vol. 29, no. 5, pp. 1137–1141, 2006.
[9]  L. Zhou and G. Hu, “Global exponential periodicity and stability of cellular neural networks with variable and distributed delays,” Applied Mathematics and Computation, vol. 195, no. 2, pp. 402–411, 2008.
[10]  Y. Li, W. Xing, and L. Lu, “Existence and global exponential stability of periodic solution of a class of neural networks with impulses,” Chaos, Solitons and Fractals, vol. 27, no. 2, pp. 437–445, 2006.
[11]  B. Liu and L. Huang, “Existence and exponential stability of periodic solutions for cellular neural networks with time-varying delays,” Physics Letters A, vol. 349, no. 6, pp. 474–483, 2006.
[12]  H. Jiang and J. Cao, “Global exponential stability of periodic neural networks with time-varying delays,” Neurocomputing, vol. 70, no. 1–3, pp. 343–350, 2006.
[13]  J. Cao and Q. Li, “On the exponential stability and periodic solutions of delayed cellular neural networks,” Journal of Mathematical Analysis and Applications, vol. 252, no. 1, pp. 50–64, 2000.
[14]  Z. Gui and X.-S. Yang, “Stability and existence of periodic solutions of periodic cellular neural networks with time-varying delays,” Computers & Mathematics with Applications, vol. 52, no. 12, pp. 1657–1670, 2006.
[15]  Y. Li, “Global stability and existence of periodic solutions of discrete delayed cellular neural networks,” Physics Letters A, vol. 333, no. 1-2, pp. 51–61, 2004.
[16]  Z. Liu and L. Liao, “Existence and global exponential stability of periodic solution of cellular neural networks with time-varying delays,” Journal of Mathematical Analysis and Applications, vol. 290, no. 1, pp. 247–262, 2004.
[17]  J. Zhou, Z. Liu, and G. Chen, “Dynamics of periodic delayed neural networks,” Neural Networks, vol. 17, no. 1, pp. 87–101, 2004.
[18]  F. M. Atici and D. C. Biles, “First order dynamic inclusions on time scales,” Journal of Mathematical Analysis and Applications, vol. 292, no. 1, pp. 222–237, 2004.
[19]  S. H. Saker, “Oscillation of nonlinear dynamic equations on time scales,” Applied Mathematics and Computation, vol. 148, no. 1, pp. 81–91, 2004.
[20]  M. Bohner and R. Hilscher, “An eigenvalue problem for linear Hamiltonian dynamic systems,” Polytechnica Posnaniensis, no. 35, pp. 35–49, 2005.
[21]  Y. Li, X. Chen, and L. Zhao, “Stability and existence of periodic solutions to delayed Cohen-Grossberg BAM neural networks with impulses on time scales,” Neurocomputing, vol. 72, no. 7–9, pp. 1621–1630, 2009.
[22]  Y. Li and C. Wang, “Uniformly almost periodic functions and almost periodic solutions to dynamic equations on time scales,” Abstract and Applied Analysis, vol. 2011, Article ID 341520, 22 pages, 2011.
[23]  J. Diblík, M. R??i?ková, and Z. ?marda, “Wa?ewski's method for systems of dynamic equations on time scales,” Nonlinear Analysis:Theory, Methods and Applications, vol. 71, no. 12, pp. e1124–e1131, 2009.
[24]  J. Diblík, M. R??i?ková, Z. ?marda, and Z. ?utá, “Asymptotic convergence of the solutions of a dynamic equation on discrete time scales,” Abstract and Applied Analysis, vol. 2012, Article ID 580750, 20 pages, 2012.
[25]  Y. Li and C. Wang, “Almost periodic solutions of shunting inhibitory cellular neural networks on time scales,” Communications in Nonlinear Science and Numerical Simulation, no. 17, pp. 3258–3266, 2012.
[26]  J. Diblík and I. Hlavi?ková, “Combination of Liapunov and retract methods in the investigation of the asymptotic behavior of solutions of systems of discrete equations,” Dynamic Systems and Applications, vol. 18, no. 3-4, pp. 507–537, 2009.
[27]  J. Diblík, D. Y. Khusainov, I. V. Grytsay, and Z. ?marda, “Stability of nonlinear autonomous quadratic discrete systems in the critical case,” Discrete Dynamics in Nature and Society, vol. 2010, Article ID 539087, 23 pages, 2010.
[28]  Y. Li and C. Wang, “Pseudo almost periodic functions and pseudo almost periodic solutions to dynamic equations on time scales,” Advances in Difference Equations, vol. 2012, article 77, 2012.
[29]  S. Mohamad and K. Gopalsamy, “Dynamics of a class of discrete-time neural networks and their continuous-time counterparts,” Mathematics and Computers in Simulation, vol. 53, no. 1-2, pp. 1–39, 2000.
[30]  S. Mohamad, “Global exponential stability in continuous-time and discrete-time delayed bidirectional neural networks,” Physica D, vol. 159, no. 3-4, pp. 233–251, 2001.
[31]  E. R. Kaufmann and Y. N. Raffoul, “Periodic solutions for a neutral nonlinear dynamical equation on a time scale,” Journal of Mathematical Analysis and Applications, vol. 319, no. 1, pp. 315–325, 2006.
[32]  M. Bohner and A. Peterson, Dynamic Equations on Time Scales: An Introduction with Applications, Birkh?user, Boston, Mass, USA, 2001.
[33]  M. Bohner, M. Fan, and J. Zhang, “Existence of periodic solutions in predator-prey and competition dynamic systems,” Nonlinear Analysis: Real World Applications, vol. 7, no. 5, pp. 1193–1204, 2006.
[34]  D. O'Regan, Y. J. Cho, and Y.-Q. Chen, Topological Degree Theory and Applications, vol. 10 of Mathematical Analysis and Applications, Chapman & Hall/CRC, Boca Raton, Fla, USA, 2006.
[35]  A. Berman and R. J. Plemmons, Nonnegative Matrices in the Mathematical Sciences, Academic Press, New York, NY, USA, 1979.
[36]  O. Do?ly and S. Hilger, “A necessary and sufficient condition for oscillation of the Sturm-Liouville dynamic equation on time scales,” Journal of Computational and Applied Mathematics, vol. 141, no. 1-2, pp. 147–158, 2002.

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