Abstract
This paper is concerned with the sampled-data state estimation problem for neural networks with both Markovian jumping parameters and leakage time-varying delays. Instead of the continuous measurement, the sampled measurement is used to estimate the neuron states, and a sampled-data estimator is constructed. In order to make full use of the sawtooth structure characteristic of the sampling input delay, a discontinuous Lyapunov functional is proposed based on the extended Wirtinger inequality. A less conservative delay dependent stability criterion is derived via constructing a new triple-integral Lyapunov–Krasovskii functional and the famous Jenson integral inequality. Based on the Lyapunov–Krasovskii functional approach, a state estimator of the considered neural networks has been achieved by solving some linear matrix inequalities, which can be easily facilitated by using the standard numerical software. Finally, two numerical examples are provided to show the effectiveness of the proposed methods.
Similar content being viewed by others
References
Hagan, M.T., Demuth, H.B., Beale, M.: Neural Network Design. PWS Publishing Company, Boston (1996)
Gupta, M.M., Jin, L., Homma, N.: Static and Dynamic Neural Networks: from Fundamentals to Advanced Theory. Wiley, New York (2003)
Cichoki, A., Unbehauen, R.: Neural Networks for Optimization and Signal Processing. Wiley, Chichester (1993)
Haykin, S.: Neural Networks: a Comprehensive Foundation. Prentice Hall, New York (1998)
Ahn, C.K.: Robust stability of recurrent neural networks with ISS learning algorithm. Nonlinear Dyn. 65, 413–419 (2011)
Haken, H.: Pattern recognition and synchronization in pulse-coupled neural networks. Nonlinear Dyn. 44, 269–276 (2006)
Hendzel, Z.: An adaptive critic neural network for motion control of a wheeled mobile robot. Nonlinear Dyn. 50, 849–855 (2007)
Civalleri, P.P., Gilli, M., Pandolfi, L.: On stability of cellular neural networks with delay. IEEE Trans. Circuits Syst. I, Fundam. Theory Appl. 40, 157–165 (1993)
Marcus, C.M., Westervelt, R.M.: Stability of analog neural networks with delay. Phys. Rev. A 39, 347–359 (1989)
Cao, J.: Global stability conditions for delayed CNNs. IEEE Trans. Circuits Syst. I 48, 1330–1333 (2001)
Liao, T.L., Wang, F.C.: Global stability for cellular neural networks with time delay. IEEE Trans. Neural Netw. 11, 1481–1484 (2000)
Li, X., Cao, J.: Delay-dependent stability of neural networks of neutral-type with time delay in the leakage term. Nonlinearity 23, 1709–1726 (2010)
Fu, X., Li, X., Akca, H.: Exponential state estimation for impulsive neural networks with time delay in the leakage term. Arab. J. Math. (2012). doi:10.1007/s40065-012-0045-y
Zhu, Q., Cao, J.: Robust exponential stability of Markovian jump impulsive stochastic Cohen–Grossberg neural networks with mixed time delays. IEEE Trans. Neural Netw. 21, 1314–1325 (2010)
Zhu, Q., Cao, J.: Stability analysis of Markovian jump stochastic BAM neural networks with impulsive control and mixed time delays. IEEE Trans. Neural Netw. Learn. Syst. 23, 467–479 (2012)
Van Den Driessche, P., Zou, X.: Global attractivity in delayed Hopfield neural network models. SIAM J. Appl. Math. 6, 1878–1890 (1998)
Park, J., Kwon, O.: Design of state estimator for neural networks of neutral-type. Appl. Math. Comput. 202, 360–369 (2008)
Park, J., Kwon, O.: Further results on state estimation for neural networks of neutral-type with time-varying delay. Appl. Math. Comput. 208, 69–75 (2009)
Liu, Y., Wang, Z., Liu, X.: State estimation for discrete-time Markovian jumping neural networks with mixed mode-dependent delays. Phys. Lett. A 372, 7147–7155 (2008)
Balasubramaniam, P., Lakshmanan, S., Jeeva, S.: Sathya theesar, state estimation for Markovian jumping recurrent neural networks with interval time-varying delays. Nonlinear Dyn. 60, 661–675 (2010)
Liu, Y., Wang, Z., Liang, J., Liu, X.: Synchronization and state estimation for discrete-time complex networks with distributed delays. IEEE Trans. Syst. Man Cybern., Part B, Cybern. 38, 1314–1325 (2008)
Shen, B., Wang, Z., Liu, X.: Bounded synchronization and state estimation for discrete time-varying stochastic complex networks over a finite horizon. IEEE Trans. Neural Netw. 22, 145–157 (2011)
Huang, H., Feng, G., Cao, J.: Robust state estimation for uncertain neural networks with time-varying delay. IEEE Trans. Neural Netw. 19, 1329–1339 (2008)
Liu, X., Cao, J.: Robust state estimation for neural networks with discontinuous activations. IEEE Trans. Syst. Man Cybern., Part B, Cybern. 40, 1425–1437 (2010)
Bao, H., Cao, J.: Delay-distribution-dependent state estimation for discrete-time stochastic neural networks with random delay. Neural Netw. 24, 19–28 (2011)
Huang, H., Feng, G., Cao, J.: Guaranteed performance state estimation of static neural networks with time-varying delay. Neurocomputing 74, 606–616 (2011)
Lakshmanan, S., Park, J.H., Ji, D.H., Jung, H.Y., Nagamani, G.: State estimation of neural networks with time-varying delays and Markovian jumping parameter based on passivity theory. Nonlinear Dyn. 70, 1421–1434 (2012)
Fridman, E., Shaked, U., Suplin, V.: Input/output delay approach to robust sampled-data H ∞ control. Syst. Control Lett. 54, 271–282 (2005)
Li, N., Hu, J., Hu, J., Li, L.: Exponential state estimation for delayed recurrent neural networks with sampled-data. Nonlinear Dyn. 69, 555–564 (2012)
Mikheev, Y., Sobolev, V., Fridman, E.: Asymptotic analysis of digital control systems. Autom. Remote Control 49, 1175–1180 (1988)
Astrom, K., Wittenmark, B.: Adaptive Control. Addison-Wesley, Reading (1989)
Fridman, E., Seuret, A., Richard, J.P.: Robust sampled-data stabilization of linear systems: an input delay approach. Automatica 40, 1441–1446 (2004)
Wang, Z., Ho, D.W.C., Liu, X.: State estimation for delayed neural networks. IEEE Trans. Neural Netw. 16, 279–284 (2005)
Naghshtabrizi, P., Hespanha, J., Teel, A.: Exponential stability of impulsive systems with application to uncertain sampled-data systems. Syst. Control Lett. 57, 378–385 (2008)
Zhu, X.: Stabilization for sampled-data neural network based control systems. IEEE Trans. Syst. Man Cybern., Part B, Cybern. 41, 210–221 (2011)
Naghshtabrizi, P., Hespanha, J., Teel, A.: Stability of delay impulsive systems with application to networked control systems. In: Proceedings of the 26th American Control Conference, New York, USA, July 2007
Fridman, E.: A refined input delay approach to sampled-data control. Automatica 46, 421–427 (2010)
Liu, K., Fridman, E.: Stability analysis of networked sontrol systems: a discontinuous Lyapunov functional approach. In: Proceedings of the 48th IEEE Conference on Decision and Control, Shanghai, China, December 2009
Hardy, G., Littlewood, J.E., Polya, G.: Inequalities. Cambridge University Press, Cambridge (1934)
Mirkin, L.: Some remarks on the use of time-varying delay to model sample and hold circuits. IEEE Trans. Autom. Control 52, 1109–1112 (2007)
Fridman, E.: A refined input delay approach to sampled-data control. Automatica 46, 421–427 (2010)
Kharitonov, V., Niculescu, S.I., Moreno, J., Michiels, M.: Static output feedback stabilization: necessary conditions for multiple delay controllers. IEEE Trans. Autom. Control 52, 1109–1112 (2007)
Krasovskii, N.N., Lidskii, E.A.: Analysis and design of controllers in systems with random attributes. Autom. Remote Control 22, 1021–1025 (1961)
Kim, S., Li, H., Dougherty, E.R., Chao, N., Chen, Y., Bittner, M.L., Suh, E.B.: Can Markov chain models mimic biological regulation? J. Biol. Syst. 10, 337–357 (2002)
Wang, Z., Liu, Y., Liu, X.: State estimation for jumping recurrent neural networks with discrete and distributed delays. Neural Netw. 22, 41–48 (2009)
Gopalsamy, K.: Stability and Oscillations in Delay Differential Equations of Population Dynamics. Kluwer Academic, Dordrecht (1992)
Li, X., Fu, X.: Effects of leakage time-varying delay on stability of nonlinear differential systems. Journal of Franklin Institute. doi:10.2016/j.jfranklin.2012.04.007
Gahinet, P., Nemirovski, A., Laub, A.J., Chilali, M.: LMI Control Toolbox. The Mathworks, Natick (1995)
Liu, Z., Yu, J., Xu, D., Peng, D.: Triple-integral method for the stability analysis of delayed neural networks. Neurocomputing 99, 283–289 (2013)
Acknowledgements
The work of R. Rakkiyappan was supported by NBHM Research Project under the sanctioned No: 2/48(7)/2012/NBHM(R.P.)/R and D II/12669 and Quanxin Zhu’s work was jointly supported by the National Natural Science Foundation of China (10801056), the Natural Science Foundation of Zhejiang Province (LY12F03010) and the Natural Science Foundation of Ningbo (2012A610032).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Rakkiyappan, R., Zhu, Q. & Radhika, T. Design of sampled data state estimator for Markovian jumping neural networks with leakage time-varying delays and discontinuous Lyapunov functional approach. Nonlinear Dyn 73, 1367–1383 (2013). https://doi.org/10.1007/s11071-013-0870-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11071-013-0870-3