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Author:

Yan, Hongyun (Yan, Hongyun.) | Qiao, Yuanhua (Qiao, Yuanhua.) (Scholars:乔元华) | Duan, Lijuan (Duan, Lijuan.) (Scholars:段立娟) | Miao, Jun (Miao, Jun.)

Indexed by:

EI Scopus SCIE

Abstract:

In this paper, a type of fractional-order quaternion-valued neural networks (FOQVNNs) with leakage and time-varying delays is established to simulate real-world situations, and the global Mittag-Leffler stability of the system is investigated by using the non-decomposition method. First, to avoid decomposing the system into two complex-valued systems or four real-valued systems, a new sign function for quaternion numbers is introduced based on the ones for real and complex numbers. And two novel lemmas for quaternion-valued sign function and Caputo fractional derivative are established in quaternion domain, which are used to investigate the stability of FOQVNNs. Second, a concise and flexible quaternion-valued state feedback controller is directly designed and a novel 1-norm Lyapunov function composed of the absolute values of real and imaginary parts is established. Then, based on the designed quaternion-valued state feedback controller and the proposed lemmas, some sufficient conditions are given to ensure the global Mittag-Leffler stability of the system. Finally, a numerical simulation is given to verify the theoretical results. (C) 2021 Elsevier Ltd. All rights reserved.

Keyword:

Time-varying delay Quaternion-valued neural networks Leakage delay Fractional-order Mittag-Leffler stability

Author Community:

  • [ 1 ] [Yan, Hongyun]Beijing Univ Technol, Fac Sci, Beijing 100124, Peoples R China
  • [ 2 ] [Qiao, Yuanhua]Beijing Univ Technol, Fac Sci, Beijing 100124, Peoples R China
  • [ 3 ] [Duan, Lijuan]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 4 ] [Miao, Jun]Beijing Informat Sci & Technol Univ, Sch Comp Sci, Beijing 100101, Peoples R China

Reprint Author's Address:

  • 乔元华

    [Qiao, Yuanhua]Beijing Univ Technol, Fac Sci, Beijing 100124, Peoples R China

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Related Keywords:

Source :

NEURAL NETWORKS

ISSN: 0893-6080

Year: 2021

Volume: 142

Page: 500-508

7 . 8 0 0

JCR@2022

ESI Discipline: COMPUTER SCIENCE;

ESI HC Threshold:87

JCR Journal Grade:1

Cited Count:

WoS CC Cited Count: 18

SCOPUS Cited Count: 22

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 19

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