Quasi-Projective Synchronization of Distributed-Order Recurrent Neural Networks

In this paper, the quasi-projective synchronization of distributed-order recurrent neural networks is investigated. Firstly, based on the definition of the distributed-order derivative and metric space theory, two distributed-order differential inequalities are obtained. Then, by employing the Lyapu...

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Bibliographic Details
Main Authors: Xiao Liu, Kelin Li, Qiankun Song, Xujun Yang
Format: Article
Language:English
Published: MDPI AG 2021-12-01
Series:Fractal and Fractional
Subjects:
Online Access:https://www.mdpi.com/2504-3110/5/4/260
Description
Summary:In this paper, the quasi-projective synchronization of distributed-order recurrent neural networks is investigated. Firstly, based on the definition of the distributed-order derivative and metric space theory, two distributed-order differential inequalities are obtained. Then, by employing the Lyapunov method, Laplace transform, Laplace final value theorem, and some inequality techniques, the quasi-projective synchronization sufficient conditions for distributed-order recurrent neural networks are established in cases of feedback control and hybrid control schemes, respectively. Finally, two numerical examples are given to verify the effectiveness of the theoretical results.
ISSN:2504-3110