Finite-time bipartite synchronization of coupled neural networks with uncertain parameters

Kun Mao, Xiaoyang Liu, Jinde Cao and Yuanfa Hu

Physica A: Statistical Mechanics and its Applications, 2022, vol. 585, issue C

Abstract: This paper investigates the finite-time bipartite synchronization (FTBS) of coupled neural networks (CNNs) with uncertain parameters. Under signed graphs, two control strategies are designed to guarantee FTBS of CNNs with or without external disturbances, respectively. For the disturbed CNNs, a discontinuous adaptive control strategy is proposed to overcome the impacts caused by uncertain parameters and disturbances with the help of non-smooth analysis and Lyapunov stability theory. For the undisturbed CNNs, a periodically intermittent adaptive control strategy is developed to achieve FTBS. Finally, two numerical examples are provided to demonstrate the effectiveness of theoretical results.

Keywords: Neural networks; Finite-time control; Bipartite synchronization; Uncertain parameters (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:585:y:2022:i:c:s0378437121007044

DOI: 10.1016/j.physa.2021.126431

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