Fractional delay segments method on time-delayed recurrent neural networks with impulsive and stochastic effects: An exponential stability approach

Abstract

Abstract This paper concerns with the problem of exponential stability analysis of time-delayed recurrent neural networks with impulsive and stochastic effects under fractional segments or intervals in delays. The delays in discrete term are assumed to be time-varying and different from existing literature, the discrete delay interval has been separated into fractional segments, which guarantees the availability of lower and upper bounds for feasibility with accuracy. By constructing a suitable Lyapunov–Krasovskii functional (LKF), with the aid of stability theory and inequality techniques, several novel criteria are originated via linear matrix inequalities (LMIs) to ensure the exponential stability of addressed neural networks in the mean square sense. Finally, two numerical examples are presented to substantiate the superiority and effectiveness of our theoretical outcomes.