Projective quasi-synchronization of complex-valued recurrent neural networks with proportional delay and mismatched parameters via matrix measure approach

Abstract

This article is concerned with the projective quasi-synchronization of non-identical complex-valued recurrent neural networks (CVRNNs) with proportional delays and mismatched parameters. The nonlinear Lipschitz activation functions under Lyapunov stability criteria and matrix measure approach have been employed. By designing a suitable controller, a sufficient condition for projective quasi-synchronization criteria of the non-identical CVRNNs model has been derived through the proper description of the matrix measure approach. A significant result for the CVRNNs with mismatched parameters and proportional delays is provided. Finally, a numerical simulation result is given to validate the usefulness and persistence of the theoretical results. The results for different particular cases are displayed graphically. © 2023 Elsevier Ltd