EXPONENTIAL STABILITY OF STOCHASTIC COHEN-GROSSBERG-TYPE BAM NEURAL NETWORKS WITH S-TYPE DISTRIBUTED DELAYS

LINSHAN WANG; KUISEN MA

Title	EXPONENTIAL STABILITY OF STOCHASTIC COHEN-GROSSBERG-TYPE BAM NEURAL NETWORKS WITH S-TYPE DISTRIBUTED DELAYS
Publication Type	Journal Article
Year of Publication	2015
Authors	WANG, LINSHAN, MA, KUISEN
Volume	19
Issue	3
Start Page	343
Pagination	10
Date Published	2015
ISSN	1083-2564
AMS	34K30, 34K50, 60H15
Abstract	This paper is concerned with exponential stability in mean square for stochastic Cohen-Grossberg-type BAM neural networks with S-type distributed delays. By using Lyapunov functional method and with the help of stochastic analysis technique, the sufficient conditions to guarantee the exponential stability in mean square for the neural networks are obtained. An example is given to demonstrate the advantage and applicability of the proposed results.
URL	http://www.acadsol.eu/en/articles/19/3/3.pdf
Refereed Designation	Refereed
Full Text	REFERENCES [1] M. Cohen, S. Grossberg. Absolute stability of global pattern formation and memory storage by competitive neural networks. IEEE Transactions on Circuits and System 13 (1983), 815–826. [2] L. Wang, Relayed Recurrent Neural Networks, Science Press, Beijing, 2008. [3] D. Xu, X. Wang, Z. Yang. Further results on existence-uniqueness for stochastic functional differential equation.Science China Mathematics 56 (2013), 1169-1180. [4] L. Wang, D. Xu. Global asymptotic stability of bidirectional associative memory neural networks with S-type distributed delays. International Journal of System Science 33 (2002), 495–501. [5] B. Wang, J. Jian, C. Guo. Global exponential stability of a class of BAM networks with time-varying delays and continuously distributed delays. Neurocomputing 71 (2008), 495–501. [6] Q. Song, J. Cao. Stability in Cohen-Grossberg-type bidirectional associative memory neural networks with time-varying delays. Nonlinearity 19 (2006), 1601–1617. [7] X. Li. Exponential stability of Cohen-Grossberg-type BAM neural networks with time-varying delays via impulsive control. Neurocomputing 72 (2009), 525–530. [8] X. Nie, J. Cao. Stability analysis for the generalized Cohen-Grossberg neural networks with inverse Lipschitz neuron activations. Computers and Mathematics with Applications 57 (2009), 1522–1536. [9] M. Gao, B. Cui. Global robust exponential stability of discrete-time interval BAM neural networks with time-varying delays. Applied Mathematical Modelling 33 (2009), 1270–1284. [10] H. Zhao, N. Ding. Dynamic analysis of stochastic Cohen-Grossberg neural networks with time delays. Application Mathematical Computation 183 (2006), 464–470. [11] M. Ali, P. Balasubramaniam. Robust stability of uncertain fuzzy Cohen-Grossberg BAM neural networks with time-varying delays. Expert System with Applications 36 (2009), 10583–10588. [12] H. Xiang, J. Wang, J. Cao. Almost periodic solution to Cohen-Grossberg-type BAM networks with distributed delays. Neurocomputing 72 (2009), 3751–3759. [13] X. Li, X. Fu. Global asymptotic stability of stochastic Cohen-Grossberg-type BAM neural networks with mixed delays: An LMI approach. Journal of Computational and Applied Mathematics 235 (2011), 3385–3394. [14] L. Wan, Q. Zhou. Convergence analysis of stochastic hybrid bidirectional associative memory neural networks with delays. Physics Letters A 370 (2007), 423–432. [15] R. Zhang, L. Wang. Global exponential robust stability of interval cellular neural networks with S- type distributed delays. Mathematical and Computer Modelling 346 (2009), 794–807. [16] W. Zhang, L. Wang. Global exponential robust stability of stochastic interval cellular neural networks with S-type distributed delays. Journal of Shandong University 47 (2012), 87–92.