Synchronization of Neural Networks via Periodic Self-Triggered Impulsive Control and Its Application in Image Encryption

In this article, a periodic self-triggered impulsive (PSTI) control scheme is proposed to achieve synchronization of neural networks (NNs). Two kinds of impulsive gains with constant and random values are considered, and the corresponding synchronization criteria are obtained based on tools from imp...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2022-08, Vol.52 (8), p.8246-8257
Main Authors: Tan, Xuegang, Xiang, Changcheng, Cao, Jinde, Xu, Wenying, Wen, Guanghui, Rutkowski, Leszek
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Control theory
Encryption
Image encryption
Image processing
impulsive control
Monitoring
Neural networks
neural networks (NNs)
Neurons
periodic self-triggered
Real-time systems
Sampling
Signal processing algorithms
Stability analysis
Synchronism
Synchronization
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Summary:In this article, a periodic self-triggered impulsive (PSTI) control scheme is proposed to achieve synchronization of neural networks (NNs). Two kinds of impulsive gains with constant and random values are considered, and the corresponding synchronization criteria are obtained based on tools from impulsive control, event-driven control theory, and stability analysis. The designed triggering protocol is simpler, easier to implement, and more flexible compared with some previously reported algorithms as the protocol combines the advantages of the periodic sampling and event-driven control. In addition, the chaotic synchronization of NNs via the presented PSTI sampling is further applied to encrypt images. Several examples are also utilized to illustrate the validity of the presented synchronization algorithm of NNs based on PSTI control and its potential applications in image processing.
ISSN:2168-2267
2168-2275
DOI:10.1109/TCYB.2021.3049858