Finite-time matrix projective synchronization of fractional-order memristor-based delayed neural networks with parameter uncertainty
Articles
Shangbin Xu
Anqing Normal University image/svg+xml
Bo Hu
University of Illinois Urbana-Champaign image/svg+xml
Hai Zhang
Anqing Normal University image/svg+xml
Xinbin Chen
Anqing Normal University image/svg+xml
Jinde Cao
Southeast University image/svg+xml
Published 2026-02-07
https://doi.org/10.15388/namc.2026.31.45269
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Keywords

Caputo derivative
finite-time synchronization
parameter uncertainty
fractional-order memristive-based delayed neural networks

How to Cite

Xu, S. (2026) “Finite-time matrix projective synchronization of fractional-order memristor-based delayed neural networks with parameter uncertainty”, Nonlinear Analysis: Modelling and Control, 31, pp. 1–22. doi:10.15388/namc.2026.31.45269.

Abstract

This research examines the phenomenon of finite-time matrix projective synchronization (FTMPS) in the context of two distinct fractional-order memristor-based delayed neural networks (FOMDNNs). For the FOMDNNs with indeterminate parameters, some suitable controllers are structured, and sufficient conditions for implementing the FTMPS are demonstrated through some inequality techniques and relevant lemmas pertaining to fractional calculus. The  synchronization issues under two different norm cases are fully considered. Subsequently, two numerical examples
of the FTMPS are revealed and the discrepancies in their synchronization gradually tend to zero, which shows the validity and accuracy of the obtained synchronization results. 

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