Dynamic Behavior of a Class of Six-Neuron Fractional BAM Neural Networks

In this paper, the stability and Hopf bifurcation of a six-neuron fractional BAM neural network model with multiple delays are considered. By transforming the multiple-delays model into a fractional-order neural network model with a delay through the variable substitution, we prove the conditions fo...
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Autor*in:	Weinan Li [verfasserIn] Maoxin Liao [verfasserIn] Dongsheng Li [verfasserIn] Changjin Xu [verfasserIn] Bingbing Li [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	fractional-order BAM neural networks Hopf bifurcation stability time delay

Übergeordnetes Werk:	In: Fractal and Fractional - MDPI AG, 2018, 7(2023), 7, p 520
Übergeordnetes Werk:	volume:7 ; year:2023 ; number:7, p 520

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/fractalfract7070520

Katalog-ID:	DOAJ093901038

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spelling	10.3390/fractalfract7070520 doi (DE-627)DOAJ093901038 (DE-599)DOAJ490bb54b42604468868ec5fb3f4bcae3 DE-627 ger DE-627 rakwb eng QC310.15-319 QA1-939 QA299.6-433 Weinan Li verfasserin aut Dynamic Behavior of a Class of Six-Neuron Fractional BAM Neural Networks 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, the stability and Hopf bifurcation of a six-neuron fractional BAM neural network model with multiple delays are considered. By transforming the multiple-delays model into a fractional-order neural network model with a delay through the variable substitution, we prove the conditions for the existence of Hopf bifurcation at the equilibrium point. Finally, our results are verified by numerical simulations. fractional-order BAM neural networks Hopf bifurcation stability time delay Thermodynamics Mathematics Analysis Maoxin Liao verfasserin aut Dongsheng Li verfasserin aut Changjin Xu verfasserin aut Bingbing Li verfasserin aut In Fractal and Fractional MDPI AG, 2018 7(2023), 7, p 520 (DE-627)897435656 (DE-600)2905371-7 25043110 nnns volume:7 year:2023 number:7, p 520 https://doi.org/10.3390/fractalfract7070520 kostenfrei https://doaj.org/article/490bb54b42604468868ec5fb3f4bcae3 kostenfrei https://www.mdpi.com/2504-3110/7/7/520 kostenfrei https://doaj.org/toc/2504-3110 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2023 7, p 520
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allfieldsSound	10.3390/fractalfract7070520 doi (DE-627)DOAJ093901038 (DE-599)DOAJ490bb54b42604468868ec5fb3f4bcae3 DE-627 ger DE-627 rakwb eng QC310.15-319 QA1-939 QA299.6-433 Weinan Li verfasserin aut Dynamic Behavior of a Class of Six-Neuron Fractional BAM Neural Networks 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, the stability and Hopf bifurcation of a six-neuron fractional BAM neural network model with multiple delays are considered. By transforming the multiple-delays model into a fractional-order neural network model with a delay through the variable substitution, we prove the conditions for the existence of Hopf bifurcation at the equilibrium point. Finally, our results are verified by numerical simulations. fractional-order BAM neural networks Hopf bifurcation stability time delay Thermodynamics Mathematics Analysis Maoxin Liao verfasserin aut Dongsheng Li verfasserin aut Changjin Xu verfasserin aut Bingbing Li verfasserin aut In Fractal and Fractional MDPI AG, 2018 7(2023), 7, p 520 (DE-627)897435656 (DE-600)2905371-7 25043110 nnns volume:7 year:2023 number:7, p 520 https://doi.org/10.3390/fractalfract7070520 kostenfrei https://doaj.org/article/490bb54b42604468868ec5fb3f4bcae3 kostenfrei https://www.mdpi.com/2504-3110/7/7/520 kostenfrei https://doaj.org/toc/2504-3110 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2023 7, p 520
language	English
source	In Fractal and Fractional 7(2023), 7, p 520 volume:7 year:2023 number:7, p 520
sourceStr	In Fractal and Fractional 7(2023), 7, p 520 volume:7 year:2023 number:7, p 520
format_phy_str_mv	Article
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topic_facet	fractional-order BAM neural networks Hopf bifurcation stability time delay Thermodynamics Mathematics Analysis
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authorswithroles_txt_mv	Weinan Li @@aut@@ Maoxin Liao @@aut@@ Dongsheng Li @@aut@@ Changjin Xu @@aut@@ Bingbing Li @@aut@@
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callnumber-first	Q - Science
author	Weinan Li
spellingShingle	Weinan Li misc QC310.15-319 misc QA1-939 misc QA299.6-433 misc fractional-order BAM neural networks misc Hopf bifurcation misc stability misc time delay misc Thermodynamics misc Mathematics misc Analysis Dynamic Behavior of a Class of Six-Neuron Fractional BAM Neural Networks
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topic_title	QC310.15-319 QA1-939 QA299.6-433 Dynamic Behavior of a Class of Six-Neuron Fractional BAM Neural Networks fractional-order BAM neural networks Hopf bifurcation stability time delay
topic	misc QC310.15-319 misc QA1-939 misc QA299.6-433 misc fractional-order BAM neural networks misc Hopf bifurcation misc stability misc time delay misc Thermodynamics misc Mathematics misc Analysis
topic_unstemmed	misc QC310.15-319 misc QA1-939 misc QA299.6-433 misc fractional-order BAM neural networks misc Hopf bifurcation misc stability misc time delay misc Thermodynamics misc Mathematics misc Analysis
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title	Dynamic Behavior of a Class of Six-Neuron Fractional BAM Neural Networks
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title_full	Dynamic Behavior of a Class of Six-Neuron Fractional BAM Neural Networks
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title_sort	dynamic behavior of a class of six-neuron fractional bam neural networks
callnumber	QC310.15-319
title_auth	Dynamic Behavior of a Class of Six-Neuron Fractional BAM Neural Networks
abstract	In this paper, the stability and Hopf bifurcation of a six-neuron fractional BAM neural network model with multiple delays are considered. By transforming the multiple-delays model into a fractional-order neural network model with a delay through the variable substitution, we prove the conditions for the existence of Hopf bifurcation at the equilibrium point. Finally, our results are verified by numerical simulations.
abstractGer	In this paper, the stability and Hopf bifurcation of a six-neuron fractional BAM neural network model with multiple delays are considered. By transforming the multiple-delays model into a fractional-order neural network model with a delay through the variable substitution, we prove the conditions for the existence of Hopf bifurcation at the equilibrium point. Finally, our results are verified by numerical simulations.
abstract_unstemmed	In this paper, the stability and Hopf bifurcation of a six-neuron fractional BAM neural network model with multiple delays are considered. By transforming the multiple-delays model into a fractional-order neural network model with a delay through the variable substitution, we prove the conditions for the existence of Hopf bifurcation at the equilibrium point. Finally, our results are verified by numerical simulations.
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title_short	Dynamic Behavior of a Class of Six-Neuron Fractional BAM Neural Networks
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up_date	2024-07-03T20:04:13.806Z
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Dynamic Behavior of a Class of Six-Neuron Fractional BAM Neural Networks

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