Periodic Oscillatory Phenomenon in Fractional-Order Neural Networks Involving Different Types of Delays

This research is chiefly concerned with the stability and Hopf bifurcation for newly established fractional-order neural networks involving different types of delays. By means of an appropriate variable substitution, equivalent fractional-order neural network systems involving one delay are built. B...

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Autores principales:	Nengfa Wang, Changjin Xu, Zixin Liu
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/99d2dde35e344273901e1eef936c693c
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spelling	oai:doaj.org-article:99d2dde35e344273901e1eef936c693c2021-11-08T02:35:22ZPeriodic Oscillatory Phenomenon in Fractional-Order Neural Networks Involving Different Types of Delays1563-514710.1155/2021/8685444https://doaj.org/article/99d2dde35e344273901e1eef936c693c2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/8685444https://doaj.org/toc/1563-5147This research is chiefly concerned with the stability and Hopf bifurcation for newly established fractional-order neural networks involving different types of delays. By means of an appropriate variable substitution, equivalent fractional-order neural network systems involving one delay are built. By discussing the distribution of roots of the characteristic equation of the established fractional-order neural network systems and selecting the delay as bifurcation parameter, a novel delay-independent bifurcation condition is derived. The investigation verifies that the delay is a significant parameter which has an important influence on stability nature and Hopf bifurcation behavior of neural network systems. The computer simulation plots and bifurcation graphs effectively illustrate the reasonableness of the theoretical fruits.Nengfa WangChangjin XuZixin LiuHindawi LimitedarticleEngineering (General). Civil engineering (General)TA1-2040MathematicsQA1-939ENMathematical Problems in Engineering, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939
spellingShingle	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939 Nengfa Wang Changjin Xu Zixin Liu Periodic Oscillatory Phenomenon in Fractional-Order Neural Networks Involving Different Types of Delays
description	This research is chiefly concerned with the stability and Hopf bifurcation for newly established fractional-order neural networks involving different types of delays. By means of an appropriate variable substitution, equivalent fractional-order neural network systems involving one delay are built. By discussing the distribution of roots of the characteristic equation of the established fractional-order neural network systems and selecting the delay as bifurcation parameter, a novel delay-independent bifurcation condition is derived. The investigation verifies that the delay is a significant parameter which has an important influence on stability nature and Hopf bifurcation behavior of neural network systems. The computer simulation plots and bifurcation graphs effectively illustrate the reasonableness of the theoretical fruits.
format	article
author	Nengfa Wang Changjin Xu Zixin Liu
author_facet	Nengfa Wang Changjin Xu Zixin Liu
author_sort	Nengfa Wang
title	Periodic Oscillatory Phenomenon in Fractional-Order Neural Networks Involving Different Types of Delays
title_short	Periodic Oscillatory Phenomenon in Fractional-Order Neural Networks Involving Different Types of Delays
title_full	Periodic Oscillatory Phenomenon in Fractional-Order Neural Networks Involving Different Types of Delays
title_fullStr	Periodic Oscillatory Phenomenon in Fractional-Order Neural Networks Involving Different Types of Delays
title_full_unstemmed	Periodic Oscillatory Phenomenon in Fractional-Order Neural Networks Involving Different Types of Delays
title_sort	periodic oscillatory phenomenon in fractional-order neural networks involving different types of delays
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/99d2dde35e344273901e1eef936c693c
work_keys_str_mv	AT nengfawang periodicoscillatoryphenomenoninfractionalorderneuralnetworksinvolvingdifferenttypesofdelays AT changjinxu periodicoscillatoryphenomenoninfractionalorderneuralnetworksinvolvingdifferenttypesofdelays AT zixinliu periodicoscillatoryphenomenoninfractionalorderneuralnetworksinvolvingdifferenttypesofdelays
_version_	1718443211596234752

Periodic Oscillatory Phenomenon in Fractional-Order Neural Networks Involving Different Types of Delays

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