Open Access

ARTICLE

Adaptive Fixed-Time Synchronization of Delayed Memristor-Based Neural Networks with Discontinuous Activations

Tianyuan Jia¹, Xiangyong Chen^1,2,*, Xiurong Yao^1,*, Feng Zhao¹, Jianlong Qiu¹
1 School of Automation and Electrical Engineering, and Key Laboratory of Complex Systems and Intelligent Computing in Universities of Shandong, Linyi University, Linyi, 276005, China
2 The Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, and School of Automation, China University of Geosciences, Wuhan, 430074, China
* Corresponding Authors: Xiangyong Chen. Email: ; Xiurong Yao. Email:
(This article belongs to this Special Issue: Modeling and Analysis of Autonomous Intelligence)

Computer Modeling in Engineering & Sciences 2023, 134(1), 221-239. https://doi.org/10.32604/cmes.2022.020780

Received 11 December 2021; Accepted 11 February 2022; Issue published 24 August 2022

Abstract

Fixed-time synchronization (FTS) of delayed memristor-based neural networks (MNNs) with discontinuous activations is studied in this paper. Both continuous and discontinuous activations are considered for MNNs. And the mixed delays which are closer to reality are taken into the system. Besides, two kinds of control schemes are proposed, including feedback and adaptive control strategies. Based on some lemmas, mathematical inequalities and the designed controllers, a few synchronization criteria are acquired. Moreover, the upper bound of settling time (ST) which is independent of the initial values is given. Finally, the feasibility of our theory is attested by simulation examples.

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Keywords

Memristor-based neural networks; fixed-time synchronization; mixed delays; discontinuous activations; adaptive control

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