Improved criteria of sampled-data master-slave synchronization for chaotic neural networks with actuator saturation

This paper investigates the sampled-data master-slave synchronization problem for chaotic neural networks with actuator saturation by constructing novel Lyapunov-Krasovskii functionals. The proposed functionals exploit the sampling interval from the recent sampling instant t(k) to the next sampling instant t(k+1) by using the integral vectors integral(t)(tk) e(s)ds, 2/t- t(k) integral(t)(tk) integral(tk) (s) e(u)duds, integral(tk+1)(t) e(s)ds, and 2/t(k+1)-t integral(tk+1)(t) integral(tk+1)(s) e(u)duds. Based on the proposed functionals, this paper derives sufficient criteria for the sampled-data master-slave synchronization of chaotic neural networks with actuator saturation represented using a dead zone nonlinearity. Also, the controller gain matrix of the sampled-data controller can be obtained by solving linear matrix inequalities (LMIs). The superiority and validity of the proposed criterion are verified through the numerical example obtained from the literature. (c) 2023 The Franklin Institute. Published by Elsevier Inc. All rights reserved.