Weighted Pseudo Almost-Automorphic Solutions of Quaternion-Valued Rnns With Mixed Delays

Abstract

This work deals with a nonlinear differential equation for a quaternion-valued recurrent neural network. By using the contraction mapping principle and some differential inequalities, we directly studied the existence and the global exponential stability of weighted pseudo-almost automorphic solution for this class of quaternion-valued neural networks. Here, methods were applied without a real or a complex decomposition of the equation system. In addition, an application verifying our results and its numerical simulation was given. The generated results about the weighted pseudo-almost automorphic solution of the considered model are new.