Exponential heterogeneous anti-synchronization of multi-variable discrete stochastic inertial neural networks with adaptive corrective parameter

This article departs from the conventional considerations of homogeneous and continuous structures to propose a master–slave heterogeneous frame of time-space discrete inertial neural networks with feedback control at the boundary. The aim of this paper is to consider mean squared exponential anti-synchronization of a discrete-time and -space stochastic heterogeneous inertial neural networks with adaptive corrective parameter. By using the approaches of space–time discrete Lyapunov-Krasovskii functional and linear matrix inequality, some decision theorems for the mean squared exponential anti-synchronization of the above discrete heterogeneous networks are come true based on the designation of a updated law for adaptive parameter in the slave networks and feed-back controller in the boundary. By right of adaptive updated law, the master–slave heterogeneous networks will approximately tend to be the homogeneous networks over the continuous adjustments of the corrective parameter. The findings of this article demonstrate the efficacy of the proposed approach in addressing the challenge of anti-synchronization of time-space discrete heterogeneous networks in the domains of science and engineering. Finally, a numerical example is given to clarify the feasibility of the current work.