Abstract
In this paper, stochastic global exponential stability criteria for delayed impulsive Markovian jumping reaction-diffusion Cohen-Grossberg neural networks (CGNNs for short) are obtained by using a novel Lyapunov-Krasovskii functional approach, linear matrix inequalities (LMIs for short) technique, Itô formula, Poincaré inequality and Hardy-Poincaré inequality, where the CGNNs involve uncertain parameters, partially unknown Markovian transition rates, and even nonlinear p-Laplace diffusion (p > 1). It is worth mentioning that ellipsoid domains in ℝm (m ≥ 3) can be considered in numerical simulations for the first time owing to the synthetic applications of Poincaré inequality and Hardy-Poincaré inequality. Moreover, the simulation numerical results show that even the corollaries of the obtained results are more feasible and effective than the main results of some recent related literatures in view of significant improvement in the allowable upper bounds of delays.
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References
Cohen, M. and Grossberg, S., Absolute stability and global pattern formation and parallel memory storage by competitive neural networks, IEEE Trans. Systems Man Cybernt., 13, 1983, 815–826.
Zhang, X., Wu, S. and Li, K., Delay-dependent exponential stability for impulsive Cohen-Grossberg neural networks with time-varying delays and reaction-diffusion terms, Commun. Nonlinear Sci. Numer. Simulat., 16, 2011, 1524–1532.
Wang, X. R., Rao, R. F. and Zhong, S. M., LMI approach to stability analysis of Cohen-Grossberg neural networks with p-Laplace diffusion, J. App. Math., 2012, 523812, 12 pages.
Rong, L. B., Lu, W. L. and Chen, T. P., Global exponential stability in Hopfield and bidirectional associative memory neural networks with time delays, Chin. Ann. Math. Ser. B, 25(2), 2004, 255–262.
Rakkiyappan, R. and Balasubramaniam, P., Dynamic analysis of Markovian jumping impulsive stochastic Cohen-Grossberg neural networks with discrete interval and distributed time-varying delays, Nonlinear Anal. Hybrid Syst., 3, 2009, 408–417.
Rao, R. F. and Pu, Z. L., Stability analysis for impulsive stochastic fuzzy p-Laplace dynamic equations under Neumann or Dirichlet boundary condition, Bound. Value Probl., 2013, 2013:133, 14 pages.
Balasubramaniam, P. and Rakkiyappan, R., Delay-dependent robust stability analysis for Markovian jumping stochastic Cohen-Grossberg neural networks with discrete interval and distributed time-varying delays, Nonlinear Anal. Hybrid Syst., 3, 2009, 207–214.
Zhang, H. and Wang, Y., Stability analysis of Markovian jumping stochastic Cohen-Grossberg neural networks with mixed time delays, IEEE Trans. Neural Networks, 19, 2008, 366–370.
Song, Q. K. and Cao J. D., Stability analysis of Cohen-Grossberg neural network with both time-varying and continuously distributed delays, J. Comp. Appl. Math., 197, 2006, 188–203.
Rao, R. F., Wang, X. R., Zhong, S. M. and Pu, Z. L., LMI approach to exponential stability and almost sure exponential stability for stochastic fuzzy Markovian jumping Cohen-Grossberg neural networks with nonlinear p-Laplace diffusion, J. Appl. Math., 2013, 396903, 21 pages.
Jiang, M., Shen, Y. and Liao, X., Boundedness and global exponential stability for generalized Cohen-Grossberg neural networks with variable delay, Appl. Math. Comp., 172, 2006, 379–393.
Haykin, S., Neural Networks, Prentice-Hall, Upper Saddle River, NJ, USA, 1994.
Zhu, Q. and Cao, J., Exponential stability of stochastic neural networks with both Markovian jump parameters and mixed time delays, IEEE Trans. System, Man, and Cybernt., 41, 2011, 341–353.
Zhu, Q. and Cao, J., Stability analysis for stochastic neural networks of neutral type with both Markovian jump parameters and mixed time delays, Neurocomp., 73, 2010, 2671–2680.
Zhu, Q., Yang, X. and Wang, H., Stochastically asymptotic stability of delayed recurrent neural networks with both Markovian jump parameters and nonlinear disturbances, J. Franklin Inst., 347, 2010, 1489–1510.
Zhu, Q. and Cao, J., Stochastic stability of neural networks with both Markovian jump parameters and continuously distributed delays, Discrete Dyn. Nat. Soc., 2009, 490515, 20 pages.
Zhu, Q. and Cao, J., Robust exponential stability of Markovian jump impulsive stochastic Cohen-Grossberg neural networks with mixed time delays, IEEE Trans. Neural Networks, 21, 2010, 1314–1325.
Liang X. and Wang, L. S., Exponential stability for a class of stochastic reaction-diffusion Hopfield neural networks with delays, J. Appl. Math., 2012, 693163, 12 pages.
Zhang, Y. T., Asymptotic stability of impulsive reaction-diffusion cellular neural networks with time-varying delays, J. Appl. Math., 2012, 501891, 17 pages.
Abdelmalek, S., Invariant regions and global existence of solutions for reaction-diffusion systems with a tridiagonal matrix of diffusion coefficients and nonhomogeneous boundary conditions, J. Appl. Math., 2007, 12375, 15 pages.
Higham, D. J. and Sardar, T., Existence and stability of fixed points for a discretised nonlinear reaction-diffusion equation with delay, Appl. Numer. Math., 18, 1995, 155–173.
Baranwal, V. K., Pandey, R. K., Tripathi, M. P. and Singh, O. P., An analytic algorithm for time fractional nonlinear reaction-diffusion equation based on a new iterative method, Commun. Nonlinear Sci. Numer. Simul., 17, 2012, 3906–3921.
Meral, G. and Tezer-Sezgin, M., The comparison between the DRBEM and DQM solution of nonlinear reaction-diffusion equation, Commun. Nonlinear Sci. Numer. Simul., 16, 2011, 3990–4005.
Liang, G., Blow-up and global solutions for nonlinear reaction-diffusion equations with nonlinear boundary condition, Appl. Math. Comput., 218, 2011, 3993–3999.
Chen, H., Zhang, Y. and Zhao, Y., Stability analysis for uncertain neutral systems with discrete and distributed delays, Appl. Math. Comput., 218, 2012, 11351–11361.
Sheng, L. and Yang, H., Novel global robust exponential stability criterion for uncertain BAM neural networks with time-varying delays, Chaos, Sol. & Frac., 40, 2009, 2102–2113.
Tian, J. K., Li, Y., Zhao, J. and Zhong, S. M., Delay-dependent stochastic stability criteria for Markovian jumping neural networks with mode-dependent time-varying delays and partially known transition rates, Appl. Math. Comput., 218, 2012, 5769–5781.
Rao, R. F., Zhong, S. M. and Wang, X. R., Delay-dependent exponential stability for Markovian jumping stochastic Cohen-Grossberg neural networks with p-Laplace diffusion and partially known transition rates via a differential inequality, Adv. Diff. Equations, 2013, 2013:183.
Zhang, Y. and Luo, Q., Novel stability criteria for impulsive delayed reaction-diffusion Cohen-Grossberg neural networks via Hardy-Poincaré inequality, Chaos, Sol. & Frac., 45, 2012, 1033–1040.
Zhang, Y. and Luo, Q., Global exponential stability of impulsive delayed reaction-diffusion neural networks via Hardy-Poincaré inequality, Neurocomp., 83, 2012, 198–204.
Li, Y. and Zhao, K., Robust stability of delayed reaction-diffusion recurrent neural networks with Dirichlet boundary conditions on time scales, Neurocomp., 74, 2011, 1632–1637.
Wang, K., Teng, Z. and Jiang, H., Global exponential synchronization in delayed reaction-diffusion cellular neural networks with the Dirichlet boundary conditions, Math. Comp. Modelling, 52, 2010, 12–24.
Wang, Z., Zhang, H. and Li, P., An LMI approach to stability analysis of reaction-diffusion Cohen-Grossberg neural networks concerning Dirichlet boundary conditions and distributed delays, IEEE Trans. System, Man, and Cybern., 40, 2010, 1596–1606.
Wu, A. L. and Fu, C. J., Global exponential stability of non-autonomous FCNNs with Dirichlet boundary conditions and reaction-diffusion terms, Appl. Math. Modelling, 34, 2010, 3022–3029.
Pan, J. and Zhong, S. M., Dynamic analysis of stochastic reaction-diffusion Cohen-Grossberg neural networks with delays, Adv. Diff. Equations, 2009, 410823, 18 pages.
Brezis, H. and Vazquez, J. L., Blow-up solutions of some nonlinear elliptic problems, Rev. Mat. Univ. Comp. Mad., 10, 1997, 443–469.
Wang, Y., Xie, L. and de Souza, C. E., Robust control of a class of uncertain nonlinear system, Systems Control Lett., 19, 1992, 139–149.
Kao, Y. G., Guo, J. F., Wang C. H. and Sun, X. Q., Delay-dependent robust exponential stability of Markovian jumping reaction-diffusion Cohen-Grossberg neural networks with mixed delays, J. Franklin Inst., 349(6), 2012, 1972–1988.
Rakkiyappan, R. and Balasubramaniam, P., Dynamic analysis of Markovian jumping impulsive stochastic Cohen-Grossberg neural networks with discrete interval and distributed time-varying delays, Nonlinear Anal. Hyb. Syst., 3, 2009, 408–417.
Rao, R. F., Pu, Z. L., Zhong, S. M. and Huang, J. L., On the role of diffusion behaviors in stability criterion for p-Laplace dynamical equations with infinite delay and partial fuzzy parameters under Dirichlet boundary value, J. Appl. Math., 2013, 940845, 8 pages.
Rao, R. F., Zhong, S. M. and Wang, X. R., Stochastic stability criteria with LMI conditions for Markovian jumping impulsive BAM neural networks with mode-dependent time-varying delays and nonlinear reaction-diffusion, Commun. Nonlinear Sci. Numer. Simulat., 19(1), 2014, 258–273.
Rao, R. F. and Pu, Z. L., LMI-based stability criterion of impulsive TS fuzzy dynamic equations via fixed point theory, Abstract and Applied Analysis, 2013, 261353, 9 pages.
Pu, Z. L. and Rao, R. F., Exponential robust stability of TS fuzzy stochastic p-Laplace PDEs under zero-boundary condition, Bound. Value Probl., 2013, 2013: 264, 14 pages.
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This work was supported by the National Basic Research Program of China (No. 2010CB732501), the Scientific Research Fund of Science Technology Department of Sichuan Province (Nos. 2010JY0057, 2012JYZ010), the Sichuan Educational Committee Science Foundation (Nos. 08ZB002, 12ZB349) and the Scientific Research Fund of Sichuan Provincial Education Department (Nos. 14ZA0274, 08ZB002, 12ZB349).
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Rao, R. Delay-Dependent exponential stability for nonlinear reaction-diffusion uncertain Cohen-Grossberg neural networks with partially known transition rates via Hardy-Poincaré inequality. Chin. Ann. Math. Ser. B 35, 575–598 (2014). https://doi.org/10.1007/s11401-014-0839-7
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DOI: https://doi.org/10.1007/s11401-014-0839-7