Jiang C, Tang Z, Park JH, Feng J. Matrix Measure-Based Event-Triggered Impulsive Quasi-Synchronization on Coupled Neural Networks.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024;
35:1821-1832. [PMID:
35797316 DOI:
10.1109/tnnls.2022.3185586]
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Abstract
In this article, the quasi-synchronization for a kind of coupled neural networks with time-varying delays is investigated via a novel event-triggered impulsive control approach. In view of the randomly occurring uncertainties (ROUs) in the communication channels, the global quasi-synchronization for the coupled neural networks within a given error bound is considered instead of discussing the complete synchronization. A kind of distributed event-triggered impulsive controllers is presented with considering the Bernoulli stochastic variables based on ROUs, which works at each event-triggered impulsive instant. According to the matrix measure method and the Lyapunov stability theorem, several sufficient conditions for the realization of the quasi-synchronization are successfully derived. Combining with the mathematical methodology with the formula of variation of parameters and the comparison principle for the impulsive systems with time-varying delays, the convergence rate and the synchronization error bound are precisely estimated. Meanwhile, the Zeno behaviors could be eliminated in the coupled neural network with the proposed event-triggered function. Finally, a numerical example is presented to prove the results of theoretical analysis.
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