Zhang, J.Shi, P.Qiu, J.Yang, H.2014-06-182014-06-182008Mathematical and Computer Modelling, 2008; 47(9-10):1042-10510895-7177http://hdl.handle.net/2440/83496This paper deals with the problem of exponential stability for a class of uncertain stochastic neural networks with both discrete and distributed delays (also called mixed delays). The system possesses time-varying and norm-bounded uncertainties. Based on Lyapunov–Krasovskii functional and stochastic analysis approaches, new stability criteria are presented in terms of linear matrix inequalities to guarantee the delayed neural networks to be robustly exponentially stable in the mean square for all admissible parameter uncertainties. Numerical examples are given to illustrate the effectiveness of the developed techniques.en© 2007 Elsevier Ltd. All rights reserved.Stochastic neural networksTime delaysExponential stabilityLinear matrix inequalities (LMIs)Norm-bounded uncertaintiesA new criterion for exponential stability of uncertain stochastic neural networks with mixed delaysJournal article002012793710.1016/j.mcm.2007.05.0140002555119000202-s2.0-4154909222419856Shi, P. [0000-0001-6295-0405] [0000-0001-8218-586X] [0000-0002-0864-552X] [0000-0002-1358-2367] [0000-0002-5312-5435]