Improved stochastic dissipativity of uncertain discrete-time neural networks with multiple delays and impulses

详细信息    查看全文

• 作者：R. Raja ; U. Karthik Raja ; R. Samidurai…
• 关键词：Global dissipativity ; Global exponential dissipativity ; Discrete ; time stochastic neural networks ; Impulses
• 刊名：International Journal of Machine Learning and Cybernetics
• 出版年：2015
• 出版时间：April 2015
• 年：2015
• 卷：6
• 期：2
• 页码：289-305
• 全文大小：503 KB
• 参考文献：1. Balasubramaniam P, Rakkiyappan R (2009) Delay-dependent robust stability analysis of uncertain stochastic neural networks with discrete interval and distributed time-varying delays. Neurocomputing 72: 3231-3237
  2. Boyd S, ElGhaoui L, Feron E, Balakrishnan V (1994) Linear matrix inequalities in system and control theory. SIAM, Philadelphia
  3. Cao, J, Song, Q (2006) Stability in Cohen–Grossberg-type bidirectional associative memory neural networks with time-varying delays. Nonlinearity 19: pp. 1601-1617 CrossRef
  4. Cao, J, Yuan, K, Ho, DWC, Lam, J (2006) Global point dissipativity of neural networks with mixed time-varying delays. Chaos 16: pp. 013105 CrossRef
  5. Cao, J, Wang, J (2005) Global asymptotic and robust stability of recurrent neural networks with time delays. IEEE Trans Circuits Syst I 52: pp. 417-426 CrossRef
  6. Chen, W-H, Zheng, WX (2008) Improved delay-dependent asymptotic stability criteria for delayed neural networks. IEEE Trans Neural Netw 19: pp. 2154-2161 CrossRef
  7. Chen, W-H, Lu, X (2008) Mean square exponential stability of uncertain stochastic delayed neural networks. Phys Lett A 372: pp. 1061-1069 CrossRef
  8. Chunwei S, Huijun G, WeiXing Z (2009) A new approach to stability analysis of discrete-time recurrent neural networks with time-varying delay. Neurocomputing 72:2563-568.
  9. Eva K, Sivasundaram S (2011) Impulsive hybrid discrete-time Hopfield neural networks with delays and multistability analysis. Neural Netw 24:370-77
  10. Honglei X, Chen Y, Teo KL (2010) Global exponential stability of impulsive discrete-time neural networks with time-varying delays. Appl Math Comput 217:537-44
  11. Huang, YM, Xu, DY, Yang, ZC (2007) Dissipativity and periodic attractor for non-autonomous neural networks with time-varying delays. Neurocomputing 70: pp. 2953-2958 CrossRef
  12. Khargonekar, PP, Petersen, IR, Zhou, K (1990) Robust stabilization of uncertain linear systems: quadratic stabilizability and H1 control theory. IEEE Trans Autom Control 35: pp. 356-361 CrossRef
  13. Kong, Q, Liao, X (1995) Dissipation, boundedness and persistence of general ecological systems. Nonlinear Anal Theory Methods Appl 25: pp. 1237-1250 CrossRef
  14. Liao, X, Fu, Y, Guo, Y (1993) Partial dissipative property for a class of nonlinear systems with separated variables. J Math Anal Appl 173: pp. 103-115 CrossRef
  15. Liao, X, Wang, J (2003) Global dissipativity of continuous-time recurrent neural networks with time delay. Phys Rev E 68: pp. 016118 CrossRef
  16. Li, HY, Wang, C, Shi, P, Gao, HJ (2010) New passivity results for uncertain discrete-time stochastic neural networks with mixed time delays. Neurocomputing 73: pp. 3291-3299 CrossRef
  17. Li HY, Chen B, Zhou Q, Fang S (2008) Robust exponential stability for uncertain stochastic neural networks with discrete and distributed time-varying delays. Phys Lett A 372:3385-394.
  18. Li, T, Luo, Q, Sun, CY, Zhang, BY (2009) Exponential stability of recurrent neural networks with time-varying discrete and distributed delays. Nonlinear Anal Real World Appl 10: pp. 2581-2589 CrossRef
  19. Meng Z, Shouming Z, Rongjun W, Wei K (2009) Robust stability analysis for discrete-time stochastic neural networks systems with time-varying delays, Appl Math Comput 209:305-13.
  20. Raja, R, Marshal Anthoni, S (2011) Global exponential stability of BAM neural networks with time-varying delays: the discrete-time case. Commun Nonlinear Sci Numer Simulat 16: pp. 613-62
• 刊物类别：Engineering
• 刊物主题：Artificial Intelligence and Robotics
  Statistical Physics, Dynamical Systems and Complexity
  Computational Intelligence
  Control , Robotics, Mechatronics
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1868-808X

文摘

This paper investigates the problem of global dissipativity and global exponential dissipativity for a class of uncertain discrete-time stochastic neural networks with multiple time-varying delays. Here the multiple time-varying delays are assumed to be discrete and distributed and the uncertainties are assumed to be time-varying norm-bounded parameter uncertainties. By choosing a novel Lyapunov functional, combining with linear matrix inequality technique (LMI), Jensen’s inequality and stochastic analysis method, a new delay-dependent global dissipativity criterion is obtained in the form of LMI, which can be easily verified numerically using the effective LMI toolbox in Matlab. One important feature presents in our paper is that without employing model transformation and free weighting matrices our obtained result leads to less conservatism. Two illustrative examples are given to show the usefulness of the obtained dissipativity conditions.