Node-to-node clustering asymptotic synchronized discrete stochastic neural networks in time and space with Bernoulli switching delay

Abstract

The article proposes a new approach to synchronizing space–time discrete stochastic neural networks with random switching delays. This is achieved by employing a control in the boundary, which is based on node-to-node clustering and controlling theories. The design of the control in the boundary for synchronizing space–time discrete stochastic neural networks in the form of clusters is based on the establishment of several significant sequential inequalities and the creation of cluster information. Also, an executable computer algorithm has been developed to streamline the implementation of the findings presented in this paper. The current study represents a pioneering approach in considering spatial discrete factors, providing a solid foundation for future research and offering theoretical and practical guidance.