Non-fragile state estimation for discrete Markovian jumping neural networks

Neurocomputing

Volumn 179, Issue , 2016, Pages 238-245

  Hou, Nan ^a   Dong, Hongli ^a   Wang, Zidong ^b,c   Ren, Weijian ^a   Alsaadi, Fuad E ^c  

^a NORTHEAST PETROLEUM UNIVERSITY   (China)

^b BRUNEL UNIVERSITY   (United Kingdom)

^c KING ABDULAZIZ UNIVERSITY   (Saudi Arabia)

Author keywords

Estimator gain variations; Markovian jumping; Non fragile state estimation; Nonlinearity; Time delays

Indexed keywords

CLOSED LOOP SYSTEMS; CONVEX OPTIMIZATION; ESTIMATION; LYAPUNOV FUNCTIONS; MARKOV PROCESSES; NEURAL NETWORKS; NUMERICAL METHODS; OPTIMIZATION; STATE ESTIMATION; STOCHASTIC SYSTEMS; TIME DELAY; UNCERTAIN SYSTEMS;

CONVEX OPTIMIZATION PROBLEMS; DISCRETE-TIME NEURAL NETWORKS; GAIN VARIATIONS; LYAPUNOV-KRASOVSKII FUNCTIONALS; MARKOVIAN JUMPING; MARKOVIAN JUMPING PARAMETERS; NON-FRAGILE; NONLINEARITY;

PARAMETER ESTIMATION;

ARTICLE; ARTIFICIAL NEURAL NETWORK; LINEAR MATRIX INEQUALITY; LYAPUNOV KRASOVSKII FUNCTION; MARKOVIAN JUMPING; MATHEMATICAL PHENOMENA; NON FRAGILE STATE ESTIMATION; PRIORITY JOURNAL; PROBABILITY; SIMULATION; STATISTICAL PARAMETERS; STATISTICS; TIME VARYING DELAY;

EID: 84955744228     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2015.11.089     Document Type: Article

References (40)

1. Boyd S., Ghaoui L.E., Feron E., Balakrishnan V. Linear Matrix Inequalities in System and Control Theory 1994, SIAM, Philadelphia, PA.
2. P. Dorato, Non-fragile controller design, an overview, in: Proceedings of American Control Conference, Philadephia, Pennsylvania, 1998, pp. 2829-2831.
3. Elanayar V., Shin Y. Radial basis function neural network for approximation and estimation of nonlinear stochastic dynamic systems. IEEE Trans. Neural Netw. 1994, 5(4):594-603.
4. Farahani M., Zare Bidaki A.R., Enshaeieh M. Intelligent control of a DC motor using a self-constructing wavelet neural network. Syst. Sci. Control Eng.: Open Access J. 2014, 2(1):261-267.
5. R. Habtom, L. Litz, Estimation of unmeasured inputs using recurrent neural networks and the extended Kalman filter, in: Proceedings of the International Conference on Neural Networks, Houston, USA, vol. 4, 1997, pp. 2067-2071.
6. Hu J., Wang Z., Shen B., Gao H. Gain-constrained recursive filtering with stochastic nonlinearities and probabilistic sensor delays. IEEE Trans. Signal Process. 2013, 61(5):1230-1238.
7. Hu J., Chen D., Du J. State estimation for a class of discrete nonlinear systems with randomly occurring uncertainties and distributed sensor delays. Int. J. General Syst. 2014, 43(3-4):387-401.
8. Huang H., Feng G., Cao J. Robust state estimation for uncertain neural networks with time-varying delay. IEEE Trans. Neural Netw. 2008, 19(8):1329-1339.
9. Jian J., Zhao Z. Global stability in Lagrange sense for BAM-type Cohen-Grossberg neural networks with time-varying delays. Syst. Sci. Control Eng.: Open Access J. 2015, 3(1):1-7.
10. H. Dong, Z. Wang, S.X. Ding, H. Gao, On H∞ estimation of randomly occurring faults for a class of nonlinear time-varying systems with fading channels, IEEE Trans. Autom. Control, in press. doi:10.1109/TAC.2015.2437526.
11. Kchaou M., Souissi M., Toumi A. A new approach to non-fragile observer-based control for discrete-time fuzzy sysems. Int. J. Syst. Sci. 2012, 43(1):9-20.
12. Hu J., Wang Z., Shen B., Gao H. Quantized recursive filtering for a class of nonlinear systems with multiplicative noises and missing measurements. Int. J. Control 2013, 86(4):650-663.
13. ∞ control with randomly occurring gain variations, distributed delays and channel fadings. IET Control Theory Appl. 2015, 9(2):222-231.
14. Li L., Jia Y. Non-fragile guaranteed cost control for uncertain stochastic nonlinear time-delay systems. IET Control Theory Appl. 2009, 3(8):995-1005.
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. 2009, 20(7):1102-1116.
16. Liu Y., Wang Z., Liu X. State estimation for discrete-time Markovian jumping neural networks with mixed mode-dependent delays. Phys. Lett. 2008, 372(48):7147-7155.
17. F.M. Salam, J. Zhang, Adaptive neural observer with forward co-state propagation, in: Proceedings of the International Joint Conference on Neural Networks (IJCNN01), Washington, USA, vol. 1, 2001, pp. 675-680.
18. ∞ and passivity state estimation of memristive neural networks with random gain fluctuations. Neurocomputing 2015, 168:1111-1120.
19. ∞ state estimation for complex networks with uncertain inner coupling and incomplete measurements. IEEE Trans. Neural Netw. Learn. Syst. 2013, 24(12):2027-2037.
20. ∞ filtering for polynomial nonlinear stochastic systems in sensor networks. IEEE Trans. Ind. Electron. 2011, 58(5):1971-1979.
21. Tino P., Cernansky M., Benuskova L. Markovian architectural bias of recurrent neural networks. IEEE Trans. Neural Netw. 2004, 15(1):6-15.
22. Wang Z., Ho D.W.C., Liu X. State estimation for delayed neural networks. IEEE Trans. Neural Netw. 2005, 16(1):279-284.
23. Wang Z., Liu Y., Liu X. On global asymptotic stability of neural networks with discrete and distributed delays. Phys. Lett. A 2005, 345(4-6):299-308.
24. Dong H., Wang Z., Ding S.X., Gao H. Finite-horizon reliable control with randomly occurring uncertainties and nonlinearities subject to output quantization. Automatica 2015, 52:355-362.
25. Wang Z., Liu Y., Yu L., Liu X. Exponential stability of delayed recurrent neural networks with Markovian jumping parameters. Phys. Lett. A 2006, 356(4-5):346-352.
26. Yang G., Wang J. Robust nonfragile Kalman filtering for uncertain linear systems with estimation gain uncertainty. IEEE Trans. Autom. Control 2001, 46(2):343-348.
27. Dong H., Wang Z., Ding S.X., Gao H. Finite-horizon estimation of randomly occurring faults for a class of nonlinear time-varying systems. Automatica 2014, 50(12):3182-3189.
28. Yang X., Cao J., Lu J. Synchronization of randomly coupled neural networks with Markovian jumping and time-delay. IEEE Trans. Circuits Syst. I - Regul. Pap. 2013, 60(2):363-376.
29. S. Zhang, Z. Wang, D. Ding, H. Dong, F.E. Alsaadi, T. Hayat, Non-fragile H∞ fuzzy filtering with randomly occurring gain variations and channel fadings, IEEE Trans. Fuzzy Syst., in press. doi:10.1109/TFUZZ.2015.2446509.
30. ∞ control for discrete time-varying systems with randomly occurring nonlinearities and fading measurements. IEEE Trans. Autom. Control 2015, 60(9):2488-2493.
31. ∞ filtering for Markovian jump systems with randomly occurred nonlinearities and sensor saturation. the finite-horizon case. IEEE Trans. Signal Process 2011, 59(7):3048-3057.
32. ∞ state estimation with fading measurements, randomly varying nonlinearities and probabilistic distributed delays. Int. J. Robust Nonlinear Control 2015, 25(13):2180-2195.
33. ∞ filtering with fading measurements and randomly occurring nonlinearities. the finite horizon case. Automatica 2015, 55:37-45.
34. ∞ filter design for a class of nonlinear time-varying systems with fading channels and multiplicative noises. IEEE Trans. Signal Process. 2015, 63(13):3387-3395.
35. Li H., Chen B., Zhou Q., et al. Robust exponential stability for uncertain stochastic neural networks with discrete and distributed time-varying delays. Phys. Lett. A 2008, 372(19):3385-3394.
36. T. Wang, H. Gao, J. Qiu, A combined adaptive neural network and nonlinear model predictive control for multirate networked industrial process control, IEEE Trans. Neural Netw. Learn. Syst., 2015. doi:10.1109/TNNLS.2015.2411671.
37. Zhou Q., Shi P., Tian Y., Wang M. Approximation-Based Adaptive Tracking Control for MIMO Nonlinear Systems with Input Saturation. IEEE Trans. Cybern. 2015, 45(10):2119-2128.
38. ∞ control for a class of nonlinear Markovian jump systems with probabilistic sensor failures. Int. J. Control 2011, 84(11):1847-1857.
39. Li H., Chen B., Zhou Q., et al. Robust stability for uncertain delayed fuzzy Hopfield neural networks with Markovian jumping parameters. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 2009, 39(1):94-102.
40. ∞ sliding mode control for nonlinear stochastic systems with multiple data packet losses. Int. J. Robust Nonlinear Control 2012, 22(5):473-491.