Stability of stochastic neural networks of neutral type with Markovian jumping parameters: A delay-fractioning approach

This paper deals with the stochastically asymptotic stability in the mean square for a new class of stochastic neural networks of neutral type with both Markovian jump parameters and mixed time delays. The jumping parameters are modeled as a continuous-time, finite-state Markov chain. Based on the Lyapunov-Krasovskii functional, stochastic analysis theory and the delay-fractioning approach, the stochastically asymptotic stability of the considered neural network has been achieved by solving some linear matrix inequalities, which can be easily facilitated by using the standard numerical software. The obtained results are shown to be much less conservative via constructing a new Lyapunov-Krasovskii functional and the idea of delay fractioning. Finally, four numerical examples are provided to show the effectiveness of the proposed method. (C) 2013 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.