Synchronization results for uncertain complex-valued neural networks under delay-dependent flexible impulsive control

Abstract

This paper is concerned with error estimation between two non-identical uncertain complex-valued neural networks (CVNNs) based on delay-dependent flexible impulsive control (DDFIC). By implementing the ideas of average impulsive delay (AID) and average impulsive interval (AII), we established a new delay dependent flexible impulsive delay differential inequality to reduce the error exponentially for the proposed CVNNs. Moreover, we obtained some new criteria to reduce error exponentially and robust exponentially with regard to linear matrix inequalities (LMIs) by using the Lyapunov function for the proposed CVNNs, and we also designed the DDFIC gains by solving LMIs. The DDFIC effectively reduced the errors for the proposed networks and is represented using numerical examples along with its simulations.