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Li, X., Ge, Y., Liu, H., Li, H. and Fang, J. (2020) New Results on Synchronization of Fractional-Order Memristor-Based Neural Networks via State Feedback Control. Complexity, 2020, Article ID: 2470972.
https://doi.org/10.1155/2020/2470972

has been cited by the following article:

• TITLE: Adaptive Stochastic Synchronization of Uncertain Delayed Neural Networks

  AUTHORS: Enli Wu, Yao Wang, Fei Luo

  KEYWORDS: Neural Networks, Synchronization, Time Delays, Stochastic Perturbation, Adaptive Control

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.11 No.9, September 4, 2023

  ABSTRACT: This paper considers adaptive synchronization of uncertain neural networks with time delays and stochastic perturbation. A general adaptive controller is designed to deal with the difficulties deduced by uncertain parameters and stochastic perturbations, in which the controller is less conservative and optimal since its control gains can be automatically adjusted according to some designed update laws. Based on Lyapunov stability theory and Barbalat lemma, sufficient condition is obtained for synchronization of delayed neural networks by strict mathematical proof. Moreover, the obtained results of this paper are more general than most existing results of certainly neural networks with or without stochastic disturbances. Finally, numerical simulations are presented to substantiate our theoretical results.