Stochastic stability of neutral-type Markovian-jumping BAM neural networks with time varying delays

Abstract

In this paper, stochastic stability of neutral type Markovian-jumping bidirectional associative memory (BAM) neural networks is investigated. The jumping parameters are modeled as a continuous-time discrete-state Markov chain. The activation functions are supposed to be bounded and globally Lipschitz continuous. Furthermore, based on the Lyapunov–Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish sufficient conditions and novel delay-dependent conditions are established for the stochastic asymptotic stability of Markovian jumping BAM neural networks. The condition is presented in terms of linear matrix inequalities (LMIs), which can be easily checked by using MATLAB LMI toolbox. Finally, numerical examples are provided to show the effectiveness of the main results.