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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 8/2019

30.11.2017 | Original Article

Robust synchronization of memristor-based fractional-order Hopfield neural networks with parameter uncertainties

verfasst von: Shuxin Liu, Yongguang Yu, Shuo Zhang

Erschienen in: Neural Computing and Applications | Ausgabe 8/2019

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Abstract

A new dynamic system, the fractional-order Hopfield neural networks with parameter uncertainties based on memristor are investigated in this paper. Through constructing a suitable Lyapunov function and some sufficient conditions are established to realize the robust synchronization of such system with discontinuous right-hand based on fractional-order Lyapunov direct method. Skillfully, the closure arithmetic is employed to handle the error system and the robust synchronization is achieved by analyzing the Mittag-Leffler stability. At last, two numerical examples are given to show the effectiveness of the obtained theoretical results. The first mainly shows the chaos of the system, and the other one mainly shows the results of robust synchronization.
Vorheriger Artikel Polynomial Kernel Discriminant Analysis for 2D visualization of classification problems
Nächster Artikel An analysis approach to handle uncertain multi-criteria group decision problems in the framework of interval type-2 fuzzy sets theory

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Metadaten
Titel
Robust synchronization of memristor-based fractional-order Hopfield neural networks with parameter uncertainties
verfasst von
Shuxin Liu
Yongguang Yu
Shuo Zhang
Publikationsdatum
30.11.2017
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 8/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-017-3274-3

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