Nonlinear output control scheme for general decay synchronization of delayed neural networks with inertial term

Abstract

Summary This study is mainly concerned with the general decay synchronization (GDS) problem of a class of inertial neural networks (INNs) with time‐varying delay. First, unlike the methods used in previous relevant literature where both of the master and slave systems were transferred into the first‐order differential equations, only error dynamical system is transferred into the reduced‐order differential equation. Then, by designing a novel nonlinear output feedback controller and constructing suitable Lyapunov functionals, some simple and useful sufficient conditions ensuring the GDS of considered INNs are established via employing useful analysis techniques and linear matrix inequality approach. In addition, two examples are presented to show the effectiveness of the established theoretical results.

Delay‐dependent criteria for general decay synchronization of discontinuous fuzzy neutral‐type neural networks with time‐varying delays

“…The designed controller (3.1) can effectively deal with the time‐varying delays when investigating the general decay synchronization. Compared with the previous delay‐independent criteria for the general decay synchronization, such as Reference 19-22,24, the controller (3.1) takes more advantages.…”

“…In 2016, Wang et al 18 put forward a new concept of general decay stability, and they investigated the synchronization analysis for two classes of chaotic neural networks based on such general decay stability. After that, some results on the general decay synchronization for other neural networks have been established, see, to name a few are studies of References 19-22. For example, by introducing Lyapunov functional and inequality techniques, Abdurahman 19 considered the general decay synchronization of delayed neural networks with general activation functions.…”

Aperiodically Intermittent Control Strategy and Adaptive Synchronization of Neural Networks With Inertial and Memristive Terms

“…Now, synchronization mainly refers to the time consistency of different processes. At present, many different forms of synchronization of various neural systems have been successively proposed, for example exponential synchronization [11,12], anti-synchronization [13], delay synchronization [14], finitetime synchronization [15], fixed-time synchronization [16], projective synchronization [17], cluster synchronization [18], lag synchronization [19] and so on.…”

Exponential synchronization of coupled inertial neural networks with mixed delays via weighted integral inequalities

“…The maximum admissible sampling interval can be determined by solving a set of linear matrix inequalities (LMIs). Additionally, a novel nonlinear output feedback control scheme is designed for the general decay synchronization of inertial neural networks in Reference 19. In this article, we explore pinning control, one of frequently used synchronization controls in CDNs, 20‐22 to settle the issue.…”