Synchronization of chaotic-type delayed neural networks and its application

Sampled-Data Synchronization Control for Chaotic Neural Networks With Mixed Delays: A Discontinuous Lyapunov Functional Approach

“…. , n. (11) Remark 1: Our main objective of this paper is to design a sampled-data controller (6) to achieve that the master system (1) synchronizes with slave system (3). In other words, our purpose is to find a feedback gain matrix K such that the synchronization error system (7) is globally asymptotically stable.…”

“…Since the pioneering work of Pecora and Carroll [1], the synchronization issue of chaotic systems has attracted the attention of many researchers and has been successively applied in many engineering fields, such as secure communications [2], image encryption [3], biomedical engineering [4] and some other nonlinear fields [5]. For example, image encryption is based on sampled-data synchronization control for chaotic fuzzy cellular neural networks with different time delays was proposed in [3], and the proposed scheme is more resistant to differential attack by utilizing these obtained chaotic values to image encryption. In addition, it is well known that delayed neural networks are typical complex nonlinear systems, which can exhibit complicated dynamic behaviors and even chaotic phenomena effectively [6].…”

A Survey on Chaos Based Image Encryption Techniques

“…A lot of research was done with different techniques such as hyper-chaos (for instance, see [4,18]); with discrete mathematical models like cellular automata (see in [19][20][21][22][23]), and Substitution, a commonly used method in [12,[24][25][26][27][28]. Further, a good survey on chaos-based cryptography can be found in [29,30].…”

“…In [21], a new image encryption scheme has been presented which uses neural networks. It is formed because of the synchronization of different time delays with chaotic Fuzzy Cellular Neural Networks (FCNNS) and makes use of a sampled-data controller.…”

Exponential Synchronization of Memristive Neural Networks with Discrete and Distributed Time-Varying Delays via Event-Triggered Control

“…As another critical dynamical behavior of neural networks, synchronization has equivalent importance as stability, which is also an active topic of research due to its extensively potential applications in information science, combinatorial optimization, biological systems, and secure communication [19][20][21]. It is noteworthy that MNNs are sensitive to the initial states than the traditional neural networks, which will lead to the more complicated chaotic path.…”