Sampled-data state estimation for delayed neural networks with Markovian jumping parameters

Nonlinear Dynamics

Volumn 73, Issue 1-2, 2013, Pages 275-284

  Hu, Jiawen ^a   Li, Nan ^b   Liu, Xiaohui ^c   Zhang, Gongxuan ^c  

^a ZHEJIANG OCEAN UNIVERSITY   (China)

^b DONGHUA UNIVERSITY   (China)

^c NANJING UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Markovian chain; Neural networks; Sampled measurements; State estimation; Time delay

Indexed keywords

DELAYED NEURAL NETWORKS; ESTIMATION APPROACHES; EXPONENTIALLY STABLE; INPUT DELAY APPROACHES; LINEAR MATRIX INEQUALITY (LMIS); MARKOVIAN CHAIN; MARKOVIAN JUMPING PARAMETERS; SUFFICIENT CONDITIONS;

ESTIMATION; LINEAR MATRIX INEQUALITIES; STATE ESTIMATION; TIME DELAY;

NEURAL NETWORKS;

EID: 84879781222     PISSN: 0924090X     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11071-013-0783-1     Document Type: Article

References (28)

1. Ahn, C.K.: Delay-dependent state estimation for T-S fuzzy delayed Hopfield neural networks. Nonlinear Dyn. 61(3), 483-489 (2010)
2. Balasubramaniam, P., Lakshmanan, S., Theesar, S.J.S.: State estimation for Markovian jumping recurrent neural networks with interval time-varying delays. Nonlinear Dyn. 60(4), 661-675 (2010)
3. Balasubramaniam, P., Vembarasan, V., Rakkiyappan, R.: Delay-dependent robust exponential state estimation of Markovian jumping fuzzy Hopfield neural networks with mixed random time-varying delays. Commun. Nonlinear Sci. Numer. Simul. 16(4), 2109-2129 (2011)
4. Bao, H., Cao, J.: Delay-distribution-dependent state estimation for discrete-time stochastic neural networks with random delay. Neural Netw. 24(1), 19-28 (2011)
5. Boyed, S., Ghaoui, L.E., Feron, E., Balakrishnan, V.: Linear Matrix Inequalities in System and Control Theory. Studies in Applied Mathematics. SIAM, Philadelphia (1994)
6. ∞ control for linear-systems with Markovian jumping parameters. Control Theory Adv. Technol. 9(2), 457-466 (1993)
7. Dong, H., Wang, Z., Gao, H.: Distributed filtering for a class of time-varying systems over sensor networks with quantization errors and successive packet dropouts. IEEE Trans. Signal Process. 60(6), 3164-3173 (2012)
8. Dong, H., Wang, Z., Gao, H.: Fault detection for Markovian jump systems with sensor saturations and randomly varying nonlinearities. IEEE Trans. Circuits Syst. I, Regul. Pap. 59(10), 2354-2362 (2012)
9. Dong, H., Wang, Z., Lam, J., Gao, H.: Fuzzy-model-based robust fault detection with stochastic mixed time delays and successive packet dropouts. IEEE Trans. Syst. Man Cybern., Part B, Cybern. 42(2), 365-376 (2012)
10. Fridman, E., Seuret, A., Richard, J.P.: Robust sampled-data stabilization of linear systems: an input delay approach. Automatica 40(8), 1441-1446 (2004)
11. ∞ control for vehicle active suspension systems. IEEE Trans. Control Syst. Technol. 18(1), 238-245 (2010)
12. Gu, K.: An integral inequality in the stability problem of time-delay systems. In: Proc. 39th IEEE Conf. Decision and Control, Sydney, Australia, December 2000, pp. 2805-2810 (2000)
13. He, Y., Wang, Q., Wu, M., Lin, C.: Delay-dependent state estimation for delayed neural networks. IEEE Trans. Neural Netw. 17(4), 1077-1081 (2006)
14. Li, N., Hu, J., Hu, J., Li, L.: Exponential state estimation for delayed recurrent neural networks with sampled-data. Nonlinear Dyn. 69(1-2), 555-564 (2012)
15. Liu, Y., Wang, Z., Liang, J., Liu, X.: Stability and synchronization of discrete-time Markovian jumping neural networks with mixed mode-dependent time delays. IEEE Trans. Neural Netw. 20(7), 1102-1116 (2009)
16. Liu, Y., Wang, Z., Liu, X.: State estimation for discrete-time Markovian jumping neural networks with mixed mode-dependent delays. Phys. Lett. A 372(48), 7147-7155 (2008)
17. Park, J., Kwon, O., Lee, S.: State estimation for neural networks of neutral-type with interval time-varying delays. Appl. Math. Comput. 203(1), 217-223 (2008)
18. ∞-control for Markovian jumping linear systems with parametric uncertainty. J. Optim. Theory Appl. 95(1), 75-99 (1997)
19. Shen, B., Wang, Z., Liu, X.: Sampled-data synchronization control of dynamical networks with stochastic sampling. IEEE Trans. Autom. Control 57(10), 2644-2650 (2012)
20. Shen, B., Wang, Z., Liang, J., Liu, Y.: Recent advances on filtering and control for nonlinear stochastic complex systems with incomplete information: a survey. Math. Probl. Eng., 530759 (2012). doi: 10.1155/2012/530759
21. ∞ filtering in sensor setworks. IEEE Trans. Circuits Syst. I, Regul. Pap. 58(9), 2237-2246 (2011)
22. Tino, P., Cernansky, M., Benuskova, L.: Markovian architectural bias of recurrent neural networks. IEEE Trans. Neural Netw. 15(1), 6-15 (2004)
23. Tong, S., Shi, P.: Sampled-data filtering framework for cardiac motion recovery: optimal estimation of continuous dynamics from discrete measurements. IEEE Trans. Biomed. Eng. 54(10), 1750-1761 (2007)
24. ∞ finite-horizon control for a class of stochastic nonlinear time-varying systems subject to sensor and actuator saturations. IEEE Trans. Autom. Control 55(7), 1716-1722 (2010)
25. Wang, Z., Liu, Y., Yu, L., Liu, X.: Exponential stability of delayed recurrent neural networks with Markovian jumping parameters. Phys. Lett. A 356(4-5), 346-352 (2006)
26. ∞ control for nonlinear stochastic time-delay systems with missing measurements. IEEE Trans. Autom. Control 57(6), 1431-1444 (2012)
27. ∞ filtering with randomly occurring sensor saturations and missing measurements. Automatica 48(3), 556-562 (2012)
28. Wu, Z., Park, J.H., Su, H., Chu, J.: Passivity analysis of Markov jump neural networks with mixed time-delays and piecewise-constant transition rates. Nonlinear Anal., Real World Appl. 13(5), 2423-2431 (2012)