Global asymptotic stability of stochastic BAM neural networks with distributed delays and reaction-diffusion terms. (English) Zbl 1198.35025

Balasubramaniam, P.; Vidhya, C.

J. Comput. Appl. Math. 234, No. 12, 3458-3466 (2010).

Summary: This paper is concerned with global asymptotic stability of a class of reaction-diffusion stochastic bi-directional associative memory (BAM) neural networks with discrete and distributed delays. Based on suitable assumptions, we apply the linear matrix inequality method to propose some new sufficient stability conditions for reaction-diffusion stochastic BAM neural networks with discrete and distributed delays. The obtained results are easy to check and improve upon the existing stability results. An example is also given to demonstrate the effectiveness of the obtained results.

MSC:

35B35	Stability in context of PDEs
35K57	Reaction-diffusion equations
60H15	Stochastic partial differential equations (aspects of stochastic analysis)
35R10	Partial functional-differential equations
35R60	PDEs with randomness, stochastic partial differential equations
60H35	Computational methods for stochastic equations (aspects of stochastic analysis)

Keywords:

mixed delays; linear matrix inequality method

Software:

LMI toolbox

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