Sliding Mode Matrix-Projective Synchronization for Fractional-Order Neural Networks

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 facto...

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Bibliographic Details
Published in:Journal of mathematics (Hidawi) 2021, Vol.2021, p.1-9
Main Authors: He, Jinman, Lei, Tengfei, Jiang, Limin
Format: Article
Language:English
Subjects:
Confidentiality
Control systems design
Controllers
Design
Dynamic models
Error analysis
Feasibility studies
Forecasting
Inequality
Mathematics
Neural networks
Neurons
Parameter identification
Scaling factors
Sliding mode control
Stability analysis
Synchronism
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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.
ISSN:2314-4629
2314-4785
DOI:10.1155/2021/4562392