A novel locally active time-delay memristive Hopfield neural network and its application

Abstract Hopfield neural network is an import cornerstone of neural network research. The dynamic analysis has always been a hot topic of the Hopfield neural network research. Memristor, the fourth-generation electronic device, is considered to be ideal nonlinear device applied in neural networks. I...
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Autor*in:	Li, Ruihua [verfasserIn] Ding, Ruihua

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Anmerkung:	© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2022

Übergeordnetes Werk:	Enthalten in: European physical journal special topics - Berlin : Springer, 2007, 231(2022), 16-17 vom: 21. Apr., Seite 3005-3017
Übergeordnetes Werk:	volume:231 ; year:2022 ; number:16-17 ; day:21 ; month:04 ; pages:3005-3017

Links:	Volltext

DOI / URN:	10.1140/epjs/s11734-022-00560-3

Katalog-ID:	SPR048790362

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allfields	10.1140/epjs/s11734-022-00560-3 doi (DE-627)SPR048790362 (SPR)s11734-022-00560-3-e DE-627 ger DE-627 rakwb eng Li, Ruihua verfasserin (orcid)0000-0003-0916-3191 aut A novel locally active time-delay memristive Hopfield neural network and its application 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract Hopfield neural network is an import cornerstone of neural network research. The dynamic analysis has always been a hot topic of the Hopfield neural network research. Memristor, the fourth-generation electronic device, is considered to be ideal nonlinear device applied in neural networks. In this work, we propose a novel time-delay locally active memristor, which has abundant dynamical behaviors. Characteristics of the proposed memristor are analyzed by power-off plot, DC V–I plot and pinched hysteresis loops plot. We applied the novel time-delay locally active memristor to a Hopfield neural to investigate the dynamic of the network. It is interesting that the proposed neural network has abundant dynamic behaviors, such as coexisting attractors, chaotic attractors and hyperchaotic attractors. The interesting phenomena are illustrated through bifurcation diagram, Lyapunov exponents diagram, and phase portraits. The electrical circuit of the proposed memristor and the Hopfield neural network is designed and simulated. The circuit simulation results are well consistent with the numerical simulation. Moreover, we propose an application of the Hopfield neural network to chaotic image encryption. Histogram, correlation, information entropy, and key sensitivity show that the simple image encryption scheme has high security and reliable encryption performance. Ding, Ruihua aut Enthalten in European physical journal special topics Berlin : Springer, 2007 231(2022), 16-17 vom: 21. Apr., Seite 3005-3017 (DE-627)523571909 (DE-600)2267176-6 1951-6401 nnns volume:231 year:2022 number:16-17 day:21 month:04 pages:3005-3017 https://dx.doi.org/10.1140/epjs/s11734-022-00560-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 231 2022 16-17 21 04 3005-3017
spelling	10.1140/epjs/s11734-022-00560-3 doi (DE-627)SPR048790362 (SPR)s11734-022-00560-3-e DE-627 ger DE-627 rakwb eng Li, Ruihua verfasserin (orcid)0000-0003-0916-3191 aut A novel locally active time-delay memristive Hopfield neural network and its application 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract Hopfield neural network is an import cornerstone of neural network research. The dynamic analysis has always been a hot topic of the Hopfield neural network research. Memristor, the fourth-generation electronic device, is considered to be ideal nonlinear device applied in neural networks. In this work, we propose a novel time-delay locally active memristor, which has abundant dynamical behaviors. Characteristics of the proposed memristor are analyzed by power-off plot, DC V–I plot and pinched hysteresis loops plot. We applied the novel time-delay locally active memristor to a Hopfield neural to investigate the dynamic of the network. It is interesting that the proposed neural network has abundant dynamic behaviors, such as coexisting attractors, chaotic attractors and hyperchaotic attractors. The interesting phenomena are illustrated through bifurcation diagram, Lyapunov exponents diagram, and phase portraits. The electrical circuit of the proposed memristor and the Hopfield neural network is designed and simulated. The circuit simulation results are well consistent with the numerical simulation. Moreover, we propose an application of the Hopfield neural network to chaotic image encryption. Histogram, correlation, information entropy, and key sensitivity show that the simple image encryption scheme has high security and reliable encryption performance. Ding, Ruihua aut Enthalten in European physical journal special topics Berlin : Springer, 2007 231(2022), 16-17 vom: 21. Apr., Seite 3005-3017 (DE-627)523571909 (DE-600)2267176-6 1951-6401 nnns volume:231 year:2022 number:16-17 day:21 month:04 pages:3005-3017 https://dx.doi.org/10.1140/epjs/s11734-022-00560-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 231 2022 16-17 21 04 3005-3017
allfields_unstemmed	10.1140/epjs/s11734-022-00560-3 doi (DE-627)SPR048790362 (SPR)s11734-022-00560-3-e DE-627 ger DE-627 rakwb eng Li, Ruihua verfasserin (orcid)0000-0003-0916-3191 aut A novel locally active time-delay memristive Hopfield neural network and its application 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract Hopfield neural network is an import cornerstone of neural network research. The dynamic analysis has always been a hot topic of the Hopfield neural network research. Memristor, the fourth-generation electronic device, is considered to be ideal nonlinear device applied in neural networks. In this work, we propose a novel time-delay locally active memristor, which has abundant dynamical behaviors. Characteristics of the proposed memristor are analyzed by power-off plot, DC V–I plot and pinched hysteresis loops plot. We applied the novel time-delay locally active memristor to a Hopfield neural to investigate the dynamic of the network. It is interesting that the proposed neural network has abundant dynamic behaviors, such as coexisting attractors, chaotic attractors and hyperchaotic attractors. The interesting phenomena are illustrated through bifurcation diagram, Lyapunov exponents diagram, and phase portraits. The electrical circuit of the proposed memristor and the Hopfield neural network is designed and simulated. The circuit simulation results are well consistent with the numerical simulation. Moreover, we propose an application of the Hopfield neural network to chaotic image encryption. Histogram, correlation, information entropy, and key sensitivity show that the simple image encryption scheme has high security and reliable encryption performance. Ding, Ruihua aut Enthalten in European physical journal special topics Berlin : Springer, 2007 231(2022), 16-17 vom: 21. Apr., Seite 3005-3017 (DE-627)523571909 (DE-600)2267176-6 1951-6401 nnns volume:231 year:2022 number:16-17 day:21 month:04 pages:3005-3017 https://dx.doi.org/10.1140/epjs/s11734-022-00560-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 231 2022 16-17 21 04 3005-3017
allfieldsGer	10.1140/epjs/s11734-022-00560-3 doi (DE-627)SPR048790362 (SPR)s11734-022-00560-3-e DE-627 ger DE-627 rakwb eng Li, Ruihua verfasserin (orcid)0000-0003-0916-3191 aut A novel locally active time-delay memristive Hopfield neural network and its application 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract Hopfield neural network is an import cornerstone of neural network research. The dynamic analysis has always been a hot topic of the Hopfield neural network research. Memristor, the fourth-generation electronic device, is considered to be ideal nonlinear device applied in neural networks. In this work, we propose a novel time-delay locally active memristor, which has abundant dynamical behaviors. Characteristics of the proposed memristor are analyzed by power-off plot, DC V–I plot and pinched hysteresis loops plot. We applied the novel time-delay locally active memristor to a Hopfield neural to investigate the dynamic of the network. It is interesting that the proposed neural network has abundant dynamic behaviors, such as coexisting attractors, chaotic attractors and hyperchaotic attractors. The interesting phenomena are illustrated through bifurcation diagram, Lyapunov exponents diagram, and phase portraits. The electrical circuit of the proposed memristor and the Hopfield neural network is designed and simulated. The circuit simulation results are well consistent with the numerical simulation. Moreover, we propose an application of the Hopfield neural network to chaotic image encryption. Histogram, correlation, information entropy, and key sensitivity show that the simple image encryption scheme has high security and reliable encryption performance. Ding, Ruihua aut Enthalten in European physical journal special topics Berlin : Springer, 2007 231(2022), 16-17 vom: 21. Apr., Seite 3005-3017 (DE-627)523571909 (DE-600)2267176-6 1951-6401 nnns volume:231 year:2022 number:16-17 day:21 month:04 pages:3005-3017 https://dx.doi.org/10.1140/epjs/s11734-022-00560-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 231 2022 16-17 21 04 3005-3017
allfieldsSound	10.1140/epjs/s11734-022-00560-3 doi (DE-627)SPR048790362 (SPR)s11734-022-00560-3-e DE-627 ger DE-627 rakwb eng Li, Ruihua verfasserin (orcid)0000-0003-0916-3191 aut A novel locally active time-delay memristive Hopfield neural network and its application 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract Hopfield neural network is an import cornerstone of neural network research. The dynamic analysis has always been a hot topic of the Hopfield neural network research. Memristor, the fourth-generation electronic device, is considered to be ideal nonlinear device applied in neural networks. In this work, we propose a novel time-delay locally active memristor, which has abundant dynamical behaviors. Characteristics of the proposed memristor are analyzed by power-off plot, DC V–I plot and pinched hysteresis loops plot. We applied the novel time-delay locally active memristor to a Hopfield neural to investigate the dynamic of the network. It is interesting that the proposed neural network has abundant dynamic behaviors, such as coexisting attractors, chaotic attractors and hyperchaotic attractors. The interesting phenomena are illustrated through bifurcation diagram, Lyapunov exponents diagram, and phase portraits. The electrical circuit of the proposed memristor and the Hopfield neural network is designed and simulated. The circuit simulation results are well consistent with the numerical simulation. Moreover, we propose an application of the Hopfield neural network to chaotic image encryption. Histogram, correlation, information entropy, and key sensitivity show that the simple image encryption scheme has high security and reliable encryption performance. Ding, Ruihua aut Enthalten in European physical journal special topics Berlin : Springer, 2007 231(2022), 16-17 vom: 21. Apr., Seite 3005-3017 (DE-627)523571909 (DE-600)2267176-6 1951-6401 nnns volume:231 year:2022 number:16-17 day:21 month:04 pages:3005-3017 https://dx.doi.org/10.1140/epjs/s11734-022-00560-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 231 2022 16-17 21 04 3005-3017
language	English
source	Enthalten in European physical journal special topics 231(2022), 16-17 vom: 21. Apr., Seite 3005-3017 volume:231 year:2022 number:16-17 day:21 month:04 pages:3005-3017
sourceStr	Enthalten in European physical journal special topics 231(2022), 16-17 vom: 21. Apr., Seite 3005-3017 volume:231 year:2022 number:16-17 day:21 month:04 pages:3005-3017
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container_title	European physical journal special topics
authorswithroles_txt_mv	Li, Ruihua @@aut@@ Ding, Ruihua @@aut@@
publishDateDaySort_date	2022-04-21T00:00:00Z
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author	Li, Ruihua
spellingShingle	Li, Ruihua A novel locally active time-delay memristive Hopfield neural network and its application
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title	A novel locally active time-delay memristive Hopfield neural network and its application
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title_full	A novel locally active time-delay memristive Hopfield neural network and its application
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title_sort	novel locally active time-delay memristive hopfield neural network and its application
title_auth	A novel locally active time-delay memristive Hopfield neural network and its application
abstract	Abstract Hopfield neural network is an import cornerstone of neural network research. The dynamic analysis has always been a hot topic of the Hopfield neural network research. Memristor, the fourth-generation electronic device, is considered to be ideal nonlinear device applied in neural networks. In this work, we propose a novel time-delay locally active memristor, which has abundant dynamical behaviors. Characteristics of the proposed memristor are analyzed by power-off plot, DC V–I plot and pinched hysteresis loops plot. We applied the novel time-delay locally active memristor to a Hopfield neural to investigate the dynamic of the network. It is interesting that the proposed neural network has abundant dynamic behaviors, such as coexisting attractors, chaotic attractors and hyperchaotic attractors. The interesting phenomena are illustrated through bifurcation diagram, Lyapunov exponents diagram, and phase portraits. The electrical circuit of the proposed memristor and the Hopfield neural network is designed and simulated. The circuit simulation results are well consistent with the numerical simulation. Moreover, we propose an application of the Hopfield neural network to chaotic image encryption. Histogram, correlation, information entropy, and key sensitivity show that the simple image encryption scheme has high security and reliable encryption performance. © The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2022
abstractGer	Abstract Hopfield neural network is an import cornerstone of neural network research. The dynamic analysis has always been a hot topic of the Hopfield neural network research. Memristor, the fourth-generation electronic device, is considered to be ideal nonlinear device applied in neural networks. In this work, we propose a novel time-delay locally active memristor, which has abundant dynamical behaviors. Characteristics of the proposed memristor are analyzed by power-off plot, DC V–I plot and pinched hysteresis loops plot. We applied the novel time-delay locally active memristor to a Hopfield neural to investigate the dynamic of the network. It is interesting that the proposed neural network has abundant dynamic behaviors, such as coexisting attractors, chaotic attractors and hyperchaotic attractors. The interesting phenomena are illustrated through bifurcation diagram, Lyapunov exponents diagram, and phase portraits. The electrical circuit of the proposed memristor and the Hopfield neural network is designed and simulated. The circuit simulation results are well consistent with the numerical simulation. Moreover, we propose an application of the Hopfield neural network to chaotic image encryption. Histogram, correlation, information entropy, and key sensitivity show that the simple image encryption scheme has high security and reliable encryption performance. © The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2022
abstract_unstemmed	Abstract Hopfield neural network is an import cornerstone of neural network research. The dynamic analysis has always been a hot topic of the Hopfield neural network research. Memristor, the fourth-generation electronic device, is considered to be ideal nonlinear device applied in neural networks. In this work, we propose a novel time-delay locally active memristor, which has abundant dynamical behaviors. Characteristics of the proposed memristor are analyzed by power-off plot, DC V–I plot and pinched hysteresis loops plot. We applied the novel time-delay locally active memristor to a Hopfield neural to investigate the dynamic of the network. It is interesting that the proposed neural network has abundant dynamic behaviors, such as coexisting attractors, chaotic attractors and hyperchaotic attractors. The interesting phenomena are illustrated through bifurcation diagram, Lyapunov exponents diagram, and phase portraits. The electrical circuit of the proposed memristor and the Hopfield neural network is designed and simulated. The circuit simulation results are well consistent with the numerical simulation. Moreover, we propose an application of the Hopfield neural network to chaotic image encryption. Histogram, correlation, information entropy, and key sensitivity show that the simple image encryption scheme has high security and reliable encryption performance. © The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2022
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title_short	A novel locally active time-delay memristive Hopfield neural network and its application
url	https://dx.doi.org/10.1140/epjs/s11734-022-00560-3
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up_date	2024-07-03T21:30:03.721Z
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The dynamic analysis has always been a hot topic of the Hopfield neural network research. Memristor, the fourth-generation electronic device, is considered to be ideal nonlinear device applied in neural networks. In this work, we propose a novel time-delay locally active memristor, which has abundant dynamical behaviors. Characteristics of the proposed memristor are analyzed by power-off plot, DC V–I plot and pinched hysteresis loops plot. We applied the novel time-delay locally active memristor to a Hopfield neural to investigate the dynamic of the network. It is interesting that the proposed neural network has abundant dynamic behaviors, such as coexisting attractors, chaotic attractors and hyperchaotic attractors. The interesting phenomena are illustrated through bifurcation diagram, Lyapunov exponents diagram, and phase portraits. The electrical circuit of the proposed memristor and the Hopfield neural network is designed and simulated. The circuit simulation results are well consistent with the numerical simulation. Moreover, we propose an application of the Hopfield neural network to chaotic image encryption. Histogram, correlation, information entropy, and key sensitivity show that the simple image encryption scheme has high security and reliable encryption performance.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ding, Ruihua</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">European physical journal special topics</subfield><subfield code="d">Berlin : Springer, 2007</subfield><subfield code="g">231(2022), 16-17 vom: 21. 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A novel locally active time-delay memristive Hopfield neural network and its application

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