Stabilité Exponentielle Globale des Réseaux de Neurones Récurrents Généralisés à Retards Discrets et Distribués

Abstract

In this memoir, we deal with the problem of analyzing the global exponential stability of a class of recurrent neural networks (RNNs) with discrete and distributed mixed delays, let us consider the existence and uniqueness of équilibriste points. use a new Lyapunov-krasvsckii functional and develop a linear matrix inequality (LMI) approach to make RNNs exponentially global stable.