Sliding mode matrix-projective synchronization for fractional-order neural networks
type: Item
instance of: scholarly article
title: Sliding mode matrix-projective synchronization for fractional-order neural networks
zbMATH Open document ID: 1477.34013
DOI: 10.1155/2021/4562392
author: Jin-Man He
author: Tengfei Lei
author: Limin Jiang
published in: Journal of Mathematics
publication date: 2021-11-25T00:00:00Z
review text: Summary: This work generalizes the projection scaling factor to a general constant matrix and proposes the matrix-projection synchronization (MPS) for fractional-order neural networks (FNNs) based on sliding mode control firstly. This kind of scaling factor is far more complex than the constant scaling factor, and it is highly variable and difficult to predict in the process of realizing the synchronization for the driving and response systems, which can ensure high security and strong confidentiality. Then, the fractional-order integral sliding surface and sliding mode controller for FNNs are designed. Furthermore, the criterion for realizing MPS is proved, and the reachability and stability of the synchronization error system are analyzed, so that the global MPS is realized for FNNs. Finally, a numerical application is given to demonstrate the feasibility of theory analysis. MPS is more general, so it is reduced to antisynchronization, complete synchronization, projective synchronization (PS), and modified PS when selecting different projective matrices. This work will enrich the synchronization theory of FNNs and provide a feasible method to study the MPS of other fractional-order dynamical models.
Mathematics Subject Classification ID: 34A08
Mathematics Subject Classification ID: 92B20
zbMATH DE Number: 7433543
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2021/4562392
OpenAlex ID: W3199154306
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description: scientific article; zbMATH DE number 7433543