Periodic solutions for discrete-time Cohen–Grossberg neural networks with delays

Shang Gao; Rong Shen; Tianrui Chen

doi:10.1016/j.physleta.2018.11.016

Periodic solutions for discrete-time Cohen–Grossberg neural networks with delays

Shang Gao
, Rong Shen
, Tianrui Chen^*

^*Corresponding author for this work

School of Science

Northeast Forestry University
Harbin Institute of Technology Weihai
University of Alberta

Research output: Contribution to journal › Article › peer-review

24 Scopus citations

Abstract

In this paper, the existence of periodic solutions for general discrete-time Cohen–Grossberg neural networks with delays (DCGNND) is investigated. Based on graph theory, coincidence degree theory, and Lyapunov method, a sufficient criterion ensuring the existence of periodic solutions for DCGNND is established. In the end, an example and its numerical simulation are given to demonstrate the effectiveness of the theoretical result.

Original language	English
Pages (from-to)	414-420
Number of pages	7
Journal	Physics Letters, Section A: General, Atomic and Solid State Physics
Volume	383
Issue number	5
DOIs	https://doi.org/10.1016/j.physleta.2018.11.016
State	Published - 21 Jan 2019

Keywords

Coincidence degree theory
Discrete-time Cohen–Grossberg neural networks
Graph theory
Lyapunov method
Periodic solutions

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10.1016/j.physleta.2018.11.016

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title = "Periodic solutions for discrete-time Cohen–Grossberg neural networks with delays",

abstract = "In this paper, the existence of periodic solutions for general discrete-time Cohen–Grossberg neural networks with delays (DCGNND) is investigated. Based on graph theory, coincidence degree theory, and Lyapunov method, a sufficient criterion ensuring the existence of periodic solutions for DCGNND is established. In the end, an example and its numerical simulation are given to demonstrate the effectiveness of the theoretical result.",

keywords = "Coincidence degree theory, Discrete-time Cohen–Grossberg neural networks, Graph theory, Lyapunov method, Periodic solutions",

author = "Shang Gao and Rong Shen and Tianrui Chen",

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T1 - Periodic solutions for discrete-time Cohen–Grossberg neural networks with delays

AU - Gao, Shang

AU - Shen, Rong

AU - Chen, Tianrui

PY - 2019/1/21

Y1 - 2019/1/21

N2 - In this paper, the existence of periodic solutions for general discrete-time Cohen–Grossberg neural networks with delays (DCGNND) is investigated. Based on graph theory, coincidence degree theory, and Lyapunov method, a sufficient criterion ensuring the existence of periodic solutions for DCGNND is established. In the end, an example and its numerical simulation are given to demonstrate the effectiveness of the theoretical result.

AB - In this paper, the existence of periodic solutions for general discrete-time Cohen–Grossberg neural networks with delays (DCGNND) is investigated. Based on graph theory, coincidence degree theory, and Lyapunov method, a sufficient criterion ensuring the existence of periodic solutions for DCGNND is established. In the end, an example and its numerical simulation are given to demonstrate the effectiveness of the theoretical result.

KW - Coincidence degree theory

KW - Discrete-time Cohen–Grossberg neural networks

KW - Graph theory

KW - Lyapunov method

KW - Periodic solutions

UR - https://www.scopus.com/pages/publications/85056859415

U2 - 10.1016/j.physleta.2018.11.016

DO - 10.1016/j.physleta.2018.11.016

M3 - 文章

AN - SCOPUS:85056859415

SN - 0375-9601

VL - 383

SP - 414

EP - 420

JO - Physics Letters, Section A: General, Atomic and Solid State Physics

JF - Physics Letters, Section A: General, Atomic and Solid State Physics

IS - 5

ER -

Periodic solutions for discrete-time Cohen–Grossberg neural networks with delays

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Keywords

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