Function projective synchronization of memristor-based Cohen–Grossberg neural networks with time-varying delays

Abstract

This paper deals with the problem of function projective synchronization for a class of memristor-based Cohen-Grossberg neural networks with time-varying delays. Based on the theory of differential equations with discontinuous right-hand side, some novel criteria are obtained to realize the function projective synchronization of addressed networks by combining open loop control and linear feedback control. As some special cases, several control strategies are given to ensure the realization of complete synchronization, anti-synchronization and the stabilization of the considered memristor-based CohenGrossberg neural network. Finally, a numerical example and its simulations are provided to demonstrate the effectiveness of the obtained results.

Pinning Synchronization via Intermittent Control for Memristive Cohen-Grossberg Neural Networks With Mixed Delays

“…Especially, the studies on memristive CGNNs (MCGNNs) in the stability [27], [29], [30] and synchronization [24], [25], [31], [32] attract more attention. Among them, it is worth studying on synchronization for a class of MCGNNs in the field of secure communications, image and data encryption.…”

“…Based on a simplified MCGNNs with mixed delays obtained by a non-linear transformation, Yang et al [32] deals with the problem of exponential synchronization for the networks through a state feedback controller. Abdurahman et al [25] proposed a controller consisting of three switching open-loop controls and a linear feedback control to achieve exponential function projective synchronization for MCGNNs with timevarying delays. Chen et al [31] designed a feedback control to complete the finite-time synchronization for MCGNNs with mixed delays by using the same non-linear transformation as Yang.…”

“…Noted that there are different control methods have been applied to achieve synchronization for neural neworks such as feedback control [13], [15], [23]- [25], [31], [32], adaptive control [45], pinning control [46]- [48], impulsive control [49], sliding mode control [50], fuzzy control [51] and intermittent control [23], [51]- [54]. Abdurahman et al [23] studied that the exponential lag synchronization of CGNNs with mixed time-delays via periodically intermittent control.…”

Exponential Lagrange Stability for Markovian Jump Uncertain Neural Networks with Leakage Delay and Mixed Time-Varying Delays via Impulsive Control

“…As a special class of mathematical models, neural networks are similar to the brain synapses link structure; neural networks possess multiple dynamic behaviors [14]. For these reasons, neural frameworks have received considerable attention as a result of their intensive applications in determination of some optimization issue, associative memory, classification of patterns, and other areas [15][16][17][18]. Since axonal signal transmission time delays often occur in various neural networks and may also cause undesirable dynamic network behaviors such as oscillation and instability, thus, it is important to study the stability of neural networks.…”

Hybrid Adaptive Pinning Control for Function Projective Synchronization of Delayed Neural Networks with Mixed Uncertain Couplings

“…FPS is the driver and response system that can be synchronized up to a scaling function. Many researches mentioned that function projective synchronization (FPS) is the greater general definition of chaotic synchronization [16][17][18]. It is obvious that the definition of FPS includes comprehensive synchronization and projective synchronization.…”