Fixed-Time Stabilization for a Class of Stochastic Memristive Inertial Neural Networks With Time-Varying Delays

Abstract

This article investigates the fixed-time stabilization (XTS) of a class of stochastic memristive inertial neural networks (MINNs) with time-varying delays using interval matrix method (IMM) within the framework of the Filipov solution. To streamline the analysis, the second-order differential system is converted into an ordinary first-order differential system through suitable variable transformations. Afterwards, three types of state feedback controllers were designed. It’s worth noting that the third controller represents an improvement over the first two controllers. Consequently, we have derived several sufficient conditions for XTS. This approach results in a more conservative upper estimate for the settling time function (STF). In addition, the finite-time stabilization (FTS) criterion can be derived. Ultimately, the validity of the theoretical findings were confirmed by numerical simulation outcomes.