Optimizing calculations of coupling matrix in Hindmarsh–Rose neural network
Abstract In this paper, to research the relationship between network synchronous dynamics and the optimal coupling mode, we have constructed the coupled network structure with the Hindmarsh–Rose (HR) neuron cells as the unit and found out the optimal coupling matrix, by using the chaos ant swarm opt...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhang, Jiqian [verfasserIn] Huang, Shoufang [verfasserIn] Pang, Sitao [verfasserIn] Wang, Maosheng [verfasserIn] Gao, Sheng [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Nonlinear dynamics - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990, 84(2016), 3 vom: 02. Jan., Seite 1303-1310 |
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| Übergeordnetes Werk: |
volume:84 ; year:2016 ; number:3 ; day:02 ; month:01 ; pages:1303-1310 |
| Links: |
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| DOI / URN: |
10.1007/s11071-015-2569-0 |
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| Katalog-ID: |
SPR016384776 |
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| 520 | |a Abstract In this paper, to research the relationship between network synchronous dynamics and the optimal coupling mode, we have constructed the coupled network structure with the Hindmarsh–Rose (HR) neuron cells as the unit and found out the optimal coupling matrix, by using the chaos ant swarm optimization (CASO) algorithm. Some typical coupled networks from all the coupled configurations are selected for analysis. Furthermore, to further verify the results of the optimization algorithm, a network of 42 cell units is constructed and the synchronization behavior is compared in both the optimization and non-optimized coupling topology. Our results indicate that proper coupling matrix of HR neural network could be obtained by means of CASO algorithm. | ||
| 650 | 4 | |a HR neural network |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Synchronization |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Chaotic ant swarm optimization |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Algorithm |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Huang, Shoufang |e verfasserin |4 aut | |
| 700 | 1 | |a Pang, Sitao |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Maosheng |e verfasserin |4 aut | |
| 700 | 1 | |a Gao, Sheng |e verfasserin |4 aut | |
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10.1007/s11071-015-2569-0 doi (DE-627)SPR016384776 (SPR)s11071-015-2569-0-e DE-627 ger DE-627 rakwb eng 510 ASE 30.20 bkl Zhang, Jiqian verfasserin aut Optimizing calculations of coupling matrix in Hindmarsh–Rose neural network 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, to research the relationship between network synchronous dynamics and the optimal coupling mode, we have constructed the coupled network structure with the Hindmarsh–Rose (HR) neuron cells as the unit and found out the optimal coupling matrix, by using the chaos ant swarm optimization (CASO) algorithm. Some typical coupled networks from all the coupled configurations are selected for analysis. Furthermore, to further verify the results of the optimization algorithm, a network of 42 cell units is constructed and the synchronization behavior is compared in both the optimization and non-optimized coupling topology. Our results indicate that proper coupling matrix of HR neural network could be obtained by means of CASO algorithm. HR neural network (dpeaa)DE-He213 Synchronization (dpeaa)DE-He213 Chaotic ant swarm optimization (dpeaa)DE-He213 Algorithm (dpeaa)DE-He213 Huang, Shoufang verfasserin aut Pang, Sitao verfasserin aut Wang, Maosheng verfasserin aut Gao, Sheng verfasserin aut Enthalten in Nonlinear dynamics Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990 84(2016), 3 vom: 02. Jan., Seite 1303-1310 (DE-627)315297034 (DE-600)2012600-1 1573-269X nnns volume:84 year:2016 number:3 day:02 month:01 pages:1303-1310 https://dx.doi.org/10.1007/s11071-015-2569-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 30.20 ASE AR 84 2016 3 02 01 1303-1310 |
| spelling |
10.1007/s11071-015-2569-0 doi (DE-627)SPR016384776 (SPR)s11071-015-2569-0-e DE-627 ger DE-627 rakwb eng 510 ASE 30.20 bkl Zhang, Jiqian verfasserin aut Optimizing calculations of coupling matrix in Hindmarsh–Rose neural network 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, to research the relationship between network synchronous dynamics and the optimal coupling mode, we have constructed the coupled network structure with the Hindmarsh–Rose (HR) neuron cells as the unit and found out the optimal coupling matrix, by using the chaos ant swarm optimization (CASO) algorithm. Some typical coupled networks from all the coupled configurations are selected for analysis. Furthermore, to further verify the results of the optimization algorithm, a network of 42 cell units is constructed and the synchronization behavior is compared in both the optimization and non-optimized coupling topology. Our results indicate that proper coupling matrix of HR neural network could be obtained by means of CASO algorithm. HR neural network (dpeaa)DE-He213 Synchronization (dpeaa)DE-He213 Chaotic ant swarm optimization (dpeaa)DE-He213 Algorithm (dpeaa)DE-He213 Huang, Shoufang verfasserin aut Pang, Sitao verfasserin aut Wang, Maosheng verfasserin aut Gao, Sheng verfasserin aut Enthalten in Nonlinear dynamics Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990 84(2016), 3 vom: 02. Jan., Seite 1303-1310 (DE-627)315297034 (DE-600)2012600-1 1573-269X nnns volume:84 year:2016 number:3 day:02 month:01 pages:1303-1310 https://dx.doi.org/10.1007/s11071-015-2569-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 30.20 ASE AR 84 2016 3 02 01 1303-1310 |
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10.1007/s11071-015-2569-0 doi (DE-627)SPR016384776 (SPR)s11071-015-2569-0-e DE-627 ger DE-627 rakwb eng 510 ASE 30.20 bkl Zhang, Jiqian verfasserin aut Optimizing calculations of coupling matrix in Hindmarsh–Rose neural network 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, to research the relationship between network synchronous dynamics and the optimal coupling mode, we have constructed the coupled network structure with the Hindmarsh–Rose (HR) neuron cells as the unit and found out the optimal coupling matrix, by using the chaos ant swarm optimization (CASO) algorithm. Some typical coupled networks from all the coupled configurations are selected for analysis. Furthermore, to further verify the results of the optimization algorithm, a network of 42 cell units is constructed and the synchronization behavior is compared in both the optimization and non-optimized coupling topology. Our results indicate that proper coupling matrix of HR neural network could be obtained by means of CASO algorithm. HR neural network (dpeaa)DE-He213 Synchronization (dpeaa)DE-He213 Chaotic ant swarm optimization (dpeaa)DE-He213 Algorithm (dpeaa)DE-He213 Huang, Shoufang verfasserin aut Pang, Sitao verfasserin aut Wang, Maosheng verfasserin aut Gao, Sheng verfasserin aut Enthalten in Nonlinear dynamics Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990 84(2016), 3 vom: 02. Jan., Seite 1303-1310 (DE-627)315297034 (DE-600)2012600-1 1573-269X nnns volume:84 year:2016 number:3 day:02 month:01 pages:1303-1310 https://dx.doi.org/10.1007/s11071-015-2569-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 30.20 ASE AR 84 2016 3 02 01 1303-1310 |
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10.1007/s11071-015-2569-0 doi (DE-627)SPR016384776 (SPR)s11071-015-2569-0-e DE-627 ger DE-627 rakwb eng 510 ASE 30.20 bkl Zhang, Jiqian verfasserin aut Optimizing calculations of coupling matrix in Hindmarsh–Rose neural network 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, to research the relationship between network synchronous dynamics and the optimal coupling mode, we have constructed the coupled network structure with the Hindmarsh–Rose (HR) neuron cells as the unit and found out the optimal coupling matrix, by using the chaos ant swarm optimization (CASO) algorithm. Some typical coupled networks from all the coupled configurations are selected for analysis. Furthermore, to further verify the results of the optimization algorithm, a network of 42 cell units is constructed and the synchronization behavior is compared in both the optimization and non-optimized coupling topology. Our results indicate that proper coupling matrix of HR neural network could be obtained by means of CASO algorithm. HR neural network (dpeaa)DE-He213 Synchronization (dpeaa)DE-He213 Chaotic ant swarm optimization (dpeaa)DE-He213 Algorithm (dpeaa)DE-He213 Huang, Shoufang verfasserin aut Pang, Sitao verfasserin aut Wang, Maosheng verfasserin aut Gao, Sheng verfasserin aut Enthalten in Nonlinear dynamics Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990 84(2016), 3 vom: 02. Jan., Seite 1303-1310 (DE-627)315297034 (DE-600)2012600-1 1573-269X nnns volume:84 year:2016 number:3 day:02 month:01 pages:1303-1310 https://dx.doi.org/10.1007/s11071-015-2569-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 30.20 ASE AR 84 2016 3 02 01 1303-1310 |
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10.1007/s11071-015-2569-0 doi (DE-627)SPR016384776 (SPR)s11071-015-2569-0-e DE-627 ger DE-627 rakwb eng 510 ASE 30.20 bkl Zhang, Jiqian verfasserin aut Optimizing calculations of coupling matrix in Hindmarsh–Rose neural network 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, to research the relationship between network synchronous dynamics and the optimal coupling mode, we have constructed the coupled network structure with the Hindmarsh–Rose (HR) neuron cells as the unit and found out the optimal coupling matrix, by using the chaos ant swarm optimization (CASO) algorithm. Some typical coupled networks from all the coupled configurations are selected for analysis. Furthermore, to further verify the results of the optimization algorithm, a network of 42 cell units is constructed and the synchronization behavior is compared in both the optimization and non-optimized coupling topology. Our results indicate that proper coupling matrix of HR neural network could be obtained by means of CASO algorithm. HR neural network (dpeaa)DE-He213 Synchronization (dpeaa)DE-He213 Chaotic ant swarm optimization (dpeaa)DE-He213 Algorithm (dpeaa)DE-He213 Huang, Shoufang verfasserin aut Pang, Sitao verfasserin aut Wang, Maosheng verfasserin aut Gao, Sheng verfasserin aut Enthalten in Nonlinear dynamics Dordrecht [u.a.] : Springer Science + Business Media B.V, 1990 84(2016), 3 vom: 02. Jan., Seite 1303-1310 (DE-627)315297034 (DE-600)2012600-1 1573-269X nnns volume:84 year:2016 number:3 day:02 month:01 pages:1303-1310 https://dx.doi.org/10.1007/s11071-015-2569-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-MAT SSG-OPC-ASE 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 30.20 ASE AR 84 2016 3 02 01 1303-1310 |
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Enthalten in Nonlinear dynamics 84(2016), 3 vom: 02. Jan., Seite 1303-1310 volume:84 year:2016 number:3 day:02 month:01 pages:1303-1310 |
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HR neural network Synchronization Chaotic ant swarm optimization Algorithm |
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Zhang, Jiqian @@aut@@ Huang, Shoufang @@aut@@ Pang, Sitao @@aut@@ Wang, Maosheng @@aut@@ Gao, Sheng @@aut@@ |
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Zhang, Jiqian |
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Zhang, Jiqian ddc 510 bkl 30.20 misc HR neural network misc Synchronization misc Chaotic ant swarm optimization misc Algorithm Optimizing calculations of coupling matrix in Hindmarsh–Rose neural network |
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510 ASE 30.20 bkl Optimizing calculations of coupling matrix in Hindmarsh–Rose neural network HR neural network (dpeaa)DE-He213 Synchronization (dpeaa)DE-He213 Chaotic ant swarm optimization (dpeaa)DE-He213 Algorithm (dpeaa)DE-He213 |
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ddc 510 bkl 30.20 misc HR neural network misc Synchronization misc Chaotic ant swarm optimization misc Algorithm |
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Optimizing calculations of coupling matrix in Hindmarsh–Rose neural network |
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optimizing calculations of coupling matrix in hindmarsh–rose neural network |
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Optimizing calculations of coupling matrix in Hindmarsh–Rose neural network |
| abstract |
Abstract In this paper, to research the relationship between network synchronous dynamics and the optimal coupling mode, we have constructed the coupled network structure with the Hindmarsh–Rose (HR) neuron cells as the unit and found out the optimal coupling matrix, by using the chaos ant swarm optimization (CASO) algorithm. Some typical coupled networks from all the coupled configurations are selected for analysis. Furthermore, to further verify the results of the optimization algorithm, a network of 42 cell units is constructed and the synchronization behavior is compared in both the optimization and non-optimized coupling topology. Our results indicate that proper coupling matrix of HR neural network could be obtained by means of CASO algorithm. |
| abstractGer |
Abstract In this paper, to research the relationship between network synchronous dynamics and the optimal coupling mode, we have constructed the coupled network structure with the Hindmarsh–Rose (HR) neuron cells as the unit and found out the optimal coupling matrix, by using the chaos ant swarm optimization (CASO) algorithm. Some typical coupled networks from all the coupled configurations are selected for analysis. Furthermore, to further verify the results of the optimization algorithm, a network of 42 cell units is constructed and the synchronization behavior is compared in both the optimization and non-optimized coupling topology. Our results indicate that proper coupling matrix of HR neural network could be obtained by means of CASO algorithm. |
| abstract_unstemmed |
Abstract In this paper, to research the relationship between network synchronous dynamics and the optimal coupling mode, we have constructed the coupled network structure with the Hindmarsh–Rose (HR) neuron cells as the unit and found out the optimal coupling matrix, by using the chaos ant swarm optimization (CASO) algorithm. Some typical coupled networks from all the coupled configurations are selected for analysis. Furthermore, to further verify the results of the optimization algorithm, a network of 42 cell units is constructed and the synchronization behavior is compared in both the optimization and non-optimized coupling topology. Our results indicate that proper coupling matrix of HR neural network could be obtained by means of CASO algorithm. |
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| title_short |
Optimizing calculations of coupling matrix in Hindmarsh–Rose neural network |
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https://dx.doi.org/10.1007/s11071-015-2569-0 |
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| author2 |
Huang, Shoufang Pang, Sitao Wang, Maosheng Gao, Sheng |
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Huang, Shoufang Pang, Sitao Wang, Maosheng Gao, Sheng |
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| doi_str |
10.1007/s11071-015-2569-0 |
| up_date |
2024-07-03T22:45:49.508Z |
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|
| score |
7.4010277 |