Global asymptotic stability of cellular neural networks with proportional delays
- 359 Downloads
- 33 Citations
Abstract
Proportional delay, which is different from distributed delay, is a kind of unbounded delay. The proportional delay system as an important mathematical model often rises in some fields such as physics, biology systems, and control theory. In this paper, the uniqueness and the global asymptotic stability of equilibrium point of cellular neural networks with proportional delays are analyzed. By using matrix theory and constructing suitable Lyapunov functional, delay-dependent and delay-independent sufficient conditions are obtained for the global asymptotic stability of cellular neural networks with proportional delays. These results extend previous works on these issues for the delayed cellular neural networks. Two numerical examples and their simulation are given to illustrate the effectiveness of obtained results.
Keywords
Cellular neural networks (CNNs) Proportional delay Global asymptotic stability (GAS) Lyapunov functional Linear matrix inequality (LMI)Notes
Acknowledgments
The project is supported by the National Science Foundation of China (No. 61374009), Tianjin Municipal Education commission (No. 20100813) and Foundation for Doctors of Tianjin Normal University (No. 52LX34).
References
- 1.Ockendon, J.R., Tayler, A.B.: The dynamics of a current collection system for an electric locomotive. Proc. R. Soc. Lond. Ser. A. 322, 447–468 (1971)CrossRefGoogle Scholar
- 2.Derfel, G.A.: Kato problem for functional equational and difference Schrödinger operators. Oper. Theory Adv. Appl. 46, 319–321 (1990)MathSciNetGoogle Scholar
- 3.Fox, L., Mayers, D.F.: On a functional differential equational. J. Inst. Math. Appl. 8(3), 271–307 (1971)CrossRefMATHMathSciNetGoogle Scholar
- 4.Iserles, A.: The asymptotic behavior of certain difference equation with proportional delays. Comput. Math. Appl. 8(1–3), 141–152 (1994)CrossRefMathSciNetGoogle Scholar
- 5.Liu, Y.K.: Asymptotic behavior of functional differential equations with proportional time delays. Eur. J. Appl. Math. 7(1), 11–30 (1994)Google Scholar
- 6.Wei, J., Xu, C., Zhou, X., Li, Q.: A robust packet scheduling algorithm for proportional delay differentiation services. Comput. Commun. 29(18), 3679–3690 (2006)CrossRefGoogle Scholar
- 7.Leung, M.K.H., Lui, J.C.S., Yau, D.K.Y.: Adaptive proportional delay differentiated services: characterization and performance evaluation. IEEE/ACM Trans. Netw. 9(6), 801–817 (2001)CrossRefGoogle Scholar
- 8.Dovrolis, C., Ramanathan, P.: A case for relative differentiated services and the proportional differentiation model. IEEE Netw. 13(5), 26–34 (1999)CrossRefGoogle Scholar
- 9.Zhang, Y., Pheng, A.H., Kwong, S.L.: Convergence analysis of cellular neural networks with unbounded delay. IEEE Trans. Circuits Syst. I 48(6), 680–687 (2001)CrossRefMATHGoogle Scholar
- 10.Zhang, Q., Wei, X., Xu, J.: Delay-depend exponential stability of cellular neural networks with time-varying delays. Appl. Math. Comput. 23(4), 1363–1369 (2005)MATHMathSciNetGoogle Scholar
- 11.Liao, X., Wang, J., Zeng, Z.: Global asymptotic stability and global exponential stability of delayed cellular neural networks. IEEE Trans. Circuits Syst. II 52(7), 403–409 (2005)CrossRefGoogle Scholar
- 12.He, Y., Wu, M., She, J.: An improved global asymptotic stability criterion for delayed cellular neural neyworks. IEEE Trans. Neural Netw. 17(1), 250–252 (2006)CrossRefGoogle Scholar
- 13.Zhang, H., Wang, Z.: Global asmptotic stability of delayed cellular neyworks. IEEE Trans. Neural Netw. 18(3), 947–950 (2007)CrossRefGoogle Scholar
- 14.Zhou, L., Hu, G.: Global exponential periodicity and stability of cellular neural networks with variable and distributed delays. Appl. Math. Comput. 195(2), 402–411 (2008)CrossRefMATHMathSciNetGoogle Scholar
- 15.Balasubramaniam, P., Syedali, M., Arik, S.: Global asymptotic stability of stochastic fuzzy cellular neural networks with multi time-varying delays. Expert Syst. Appl. 37(12), 7737–7744 (2010) Google Scholar
- 16.Tan, M.: Global Asympotic stability of fuzzy cellular neural networks with unbounded distributed delays. Neural Process. Lett. 31(2), 147–157 (2010)CrossRefGoogle Scholar
- 17.Feng, Z., Lam, J.: Stability and dissipativity analysis of distributed delay cellular neural networks. IEEE Trans. Neural Netw. 22(6), 981–997 (2011)MathSciNetGoogle Scholar
- 18.Kao, Y., Gao, C.: Global exponential stability analysis for cellular neural networks with variable coffciants and delays. Neural Comput. Appl. 17(3), 291–296 (2008)CrossRefMathSciNetGoogle Scholar
- 19.Ma, K., Yu, L., Zhang, W.: Global exponential stability of cellular neural networks with time-varying discrete and distributed delays. Neurocomputing 72(10–12), 2705–2709 (2009)CrossRefGoogle Scholar
- 20.Huang, C., Cao, J.: Almost sure exponential stability of stochastic cellular neural networks with unbounded distributed delays. Neurcomputing 72(13–15), 3352–3356 (2009)CrossRefGoogle Scholar
- 21.Han, W., Liu, Y., Wang, L.S.: Global exponential stability of delayed fuzzy cellular neural networks with Markovian jumping parameters. Neural Comput. Appl. 21(1), 67–72 (2012)CrossRefMathSciNetGoogle Scholar
- 22.Ozcan, N.: A new sufficient condition for global robust stability of delayed neural networks. Neural Process. Lett. 34(3), 305–316 (2011)CrossRefGoogle Scholar
- 23.Guo, S., Huang, L.: Periodic oscillation for a class of neural networks with variable coefficients. Nonlinear Anal.: Real World Appl. 6(3), 545–561 (2005)CrossRefMATHMathSciNetGoogle Scholar
- 24.Chen, W., Zheng, W.: A new method for complete stability stability analysis of cellular neural networks with time delay. IEEE Trans. Neural Netw. 21(7), 1126–1137 (2010)Google Scholar
- 25.Chen, W., Zheng, W.: Global exponential stability of impulsive neural networks with variable delay: an LMI approach. IEEE Trans. Circuits Syst. I 56(6), 1248–1259 (2009)Google Scholar
- 26.Li, T., Song, A., Fei, S., Wang, T.: Delay-derivative-dependent stability for delayed neural networks with unbound distributed delay. IEEE Trans. Neural Netw. 21(8), 1365–1371 (2010)CrossRefGoogle Scholar
- 27.He, H., Yan, L., Tu, J.: Guaranteed cost stabilization of time-varying delay cellular neural networks via Riccati inequality approach. Neural Process. Lett 35(2), 151–158 (2012)CrossRefGoogle Scholar
- 28.Tu, J., He, H., Xiong, P.: Guaranteed cost synchronous control of time-varying delay cellular neural networks. Neural Comput. Appl. 22(1), 103–110 (2013)CrossRefGoogle Scholar
- 29.Zhang, Y., Zhou, L.: Exponential stability of a class of cellular neural networks with multi-pantograph delays. Acta Electronica Sinica 40(6), 1159–1163 (2012)Google Scholar
- 30.Zhou, L.: Dissipativity of a class of cellular neural networks with proportional delays. Nonlinear Dyn. 73(3), 1895–1903 (2013)CrossRefMATHGoogle Scholar
- 31.Zhou, L.: Delay-dependent exponential stability of cellular neural networks with multi-proportional delays. Neural Process. Lett. 38(3), 321–346 (2013)CrossRefGoogle Scholar