LMI-based finite-time boundedness analysis of neural networks with parametric uncertainties

Neurocomputing

Volumn 71, Issue 4-6, 2008, Pages 502-507

  Shen, Yanjun ^a   Li, Cuicui ^a  

^a CHINA THREE GORGES UNIVERSITY   (China)

Author keywords

Finite time boundedness; Linear matrix inequality; Neural networks; Uncertain

Indexed keywords

LINEAR MATRIX INEQUALITIES; SYSTEMS ANALYSIS; UNCERTAIN SYSTEMS;

FINITE-TIME BOUNDEDNESS; PARAMETER UNCERTAINTY;

NEURAL NETWORKS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; ASSOCIATIVE MEMORY; LINEAR MATRIX INEQUALITY; LINEAR SYSTEM; MATHEMATICAL COMPUTING; PARAMETRIC TEST; PRIORITY JOURNAL; STATISTICAL ANALYSIS; UNCERTAINTY; VALIDITY;

EID: 38649137739     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2007.07.016     Document Type: Article

References (22)

1. Amato F., Ariola M., and Dorate P. Finite-time control of linear systems subject to parameteric uncertainties and disturbances. Automatica 37 (2001) 1459-1463
2. F. Amato, M. Ariola, C. Cosentino, Finite-time control of linear time-varying systems via output feedback, in: American Control Conference, June 8-10, Portland, OR, USA, 2005.
3. Boyd S., Ghaoui L.EI., Feron E., and Balakrishnan V. Linear Matrix Inequalities in System and Control Theory (1994), SIAM, Philadelphia, PA
4. Boyd S., Ghaoui L.E., Feron E., and Balakrishnan V. Linear matrix inequality in systems and control theory. SIAM Studies in Applied Mathematics (1994), SIAM, Philadelphia
5. J. Cao, J. Lu, Adaptive synchronization of neural networks with or without time-varying delays, Chaos 16 (2006).
6. Cao J., and Zhou D. Stability analysis of delayed cellular neural networks. Neural Networks 11 (1998) 1601-1605
7. P. Dorato, Short time stability in linear time-varying systems, in: Proceeding of the IRE International Convention Record Part 4, 1961 pp. 83-87.
8. Forti M., and Tesi A. New conditions for global stability of neural networks with application to linear and quadratic programming problems. IEEE Trans. Circuits Syst. 42 (1995) 354-366
9. Forti M., Manetti S., and Marini M. Necessary and sufficient conditions for absolute stability of neural networks. IEEE Trans. Circuits Syst. 41 (1994) 491-494
10. Hopfield J.J. Neuron with graded response have collective computational properties like those of two-state neurons. Proc. Natl. Acad. Sci. 5 (1984) 3088-3092
11. Hu S.Q., and Wang J. Global exponential stability of continuous-time interval neural networks. Phys. Rev. E 65 (2002) 1-9
12. Khargoneker P.P., Petersen I.R., and Zhou K. Robust stabilization of uncertain linear systems: quadratic stabilizability and control theory. IEEE Trans. Autom. Control 35 (1990) 356-361
13. Liao X.F., Chen G., and Sanchez E.N. Delay-dependent exponential stability analysis of delayed neural networks: an LMI approach. Neural Networks 15 (2002) 855-866
14. Liao X.F., Chen G., and Sanchez E.N. LMI-based approach for asymptotically stability analysis of delayed neural networks. IEEE Trans. Circuits Syst. 49 (2002) 1033-1039
15. Liao X.F., and Li C. An LMI approach to asymptotically stability of multi-delayed neural networks. Physica D 200 (2005) 139-155
16. S. Mastellone, P. Dorato, C.T. Abdallah, Finite-time stochastic stability of discrete-time nonlinear systems: analysis and design, in: 43rd Conference on Decision and Control, 2004.
17. Orlowski P. Methods for stability evaluation for linear time varying, discrete-time system on finite time horizon. Int. J. Control 79 (2006) 249-262
18. Rong L. LMI approach for global periodicity of neural networks with time-varying delays. IEEE Trans. Circuits Syst. 52 (2005) 1451-1458
19. Sun C., and Feng C.B. Exponential periodicity of continuous-time and discrete-time neural networks with delays. Neural Process. Lett. 19 2 (2004) 131-146
20. Sun C., and Feng C.B. Exponential periodicity and stability of delayed neural networks. Math. Comput. Simulation 66 6 (2004) 469-478
21. Sun C., Zhang K., Fei S., and Feng C.B. On exponential stability of delayed neural networks with a general class of activation functions. Phys. Lett. A 298 2/3 (2002) 122-132
22. Xu S., and Lam J. A new approach to exponential stability analysis of neural networks with time-varying delays. Neural Networks 19 (2006) 76-83