Encryption-Decryption-Based Bipartite Synchronization of Markov Jump Coupled Neural Networks: An Event-Triggered Mechanism

This paper addresses the problem of encryption-decryption-based bipartite synchronization control for a class of discrete-time coupled neural networks, in which the nodes exhibit both cooperative and antagonistic interactions. Initially, a Markov chain with concealed operating modes is employed to d...

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Vydané v:IEEE internet of things journal s. 1
Hlavní autori: Shi, Liangyao, Wang, Jing, Yan, Huaicheng, Cao, Jinde, Shen, Hao
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: IEEE 2025
Predmet:
Artificial neural networks
Biological neural networks
bipartite synchronization
Directed graphs
encryption-decryption algorithm
Event detection
event-triggered mechanism
Hidden Markov models
Markov Jump coupled neural networks
Neurons
Switches
Symmetric matrices
Synchronization
Topology
ISSN:2327-4662, 2327-4662
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Shrnutí:This paper addresses the problem of encryption-decryption-based bipartite synchronization control for a class of discrete-time coupled neural networks, in which the nodes exhibit both cooperative and antagonistic interactions. Initially, a Markov chain with concealed operating modes is employed to describe Markov jump coupled neural networks with switching topologies. In this framework, a hidden Markov model is incorporated, whose emission values express the system mode. Next, the decentralized adaptive event-triggered strategy is proposed to alleviate the communication burden caused by interactions between nodes. Moreover, an encryption-decryption algorithm that takes into account identity authentication is programmed to encrypt the data at the triggering moment of each node, thereby securing the data interaction privacy. Then, the observation mode-based bipartite synchronization control law is formulated to fulfill the control demands of the plant. Furthermore, some sufficient conditions for the networks to be mean square synchronized and satisfy the H ∞ performance are obtained based on the Lyapunov stability theory. At last, two simulation examples involving chaotic neural networks are presented to verify the effectiveness of the proposed method.
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2025.3633689