Stability and Stabilization of Time-Delayed Fractional Order Neural Networks via Matrix Measure

The stability problem of delayed neural networks with fractional order dynamics has been studied in this paper. Several criteria for the stability of the equilibrium point are derived via matrix measure method and fractional order differential inequality. All criteria are formed as matrix measure, w...

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Bibliographic Details
Published inAdvances in Neural Networks - ISNN 2017 Vol. 10261; pp. 493 - 501
Main Authors Wang, Fei, Yang, Yongqing, Lu, Jianquan, Cao, Jinde
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 2017
Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Delay
Fractional-order
Matrix measure
Neural networks
Stability
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ISBN3319590715
9783319590714
ISSN0302-9743
1611-3349
DOI10.1007/978-3-319-59072-1_58

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Summary:The stability problem of delayed neural networks with fractional order dynamics has been studied in this paper. Several criteria for the stability of the equilibrium point are derived via matrix measure method and fractional order differential inequality. All criteria are formed as matrix measure, which can be easy to verify in practice. Based on which, feedback controllers are designed to stabilize a kind of chaotic fractional order neural network. Finally, two simulations are given to check the theoretical results and compare with some exist results.
Bibliography:Y. Yang—This work was jointly supported by the Natural Science Foundation of Jiangsu Province of China under Grant No. BK20161126, the Graduate Innovation Project of Jiangsu Province under Grant No. KYLX16\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{-}$$\end{document}0778.
ISBN:3319590715
9783319590714
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-59072-1_58