Adaptive Stochastic Robust Convergence of Neutral-Type Neural Networks with Markovian Jump Parameters

Abstract

The adaptive stochastic robust convergence and stability in mean square are investigated for a class of uncertain neutral-type neural networks with both Markovian jump parameters and mixed delays. The mixed delays consists of discrete and distributed time-varying delays. First, by employing the Lyapunov method and a generalized Halanay-type inequality, a delay-independent condition is derived to guarantee the state variables of the discussed neural networks to be globally uniformly exponentially stochastic convergent to a ball in the state space with a pre-specified convergence rate. Next, by applying the Jensen integral inequality and a novel Lemma, a delay-dependent criterion is developed to achieve the globally stochastic robust stability in mean square. The proposed conditions are all in terms of linear matrix inequalities, which can be solved numerically by employing the LMI toolbox in Matlab.