New Results on Finite/Fixed-Time Stabilization of Stochastic Second-Order Neutral-Type Neural Networks with Mixed Delays
Abstract This paper states with the objective of investigating the finite-time stabilization and fixed-time stabilization analysis for stochastic second-order neutral-type neural networks with mixed delays. By using a variable transformation, we first rewrite the original system as a first-order dif...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Aouiti, Chaouki [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Neural processing letters - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994, 54(2022), 6 vom: 21. Mai, Seite 5415-5437 |
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| Übergeordnetes Werk: |
volume:54 ; year:2022 ; number:6 ; day:21 ; month:05 ; pages:5415-5437 |
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| DOI / URN: |
10.1007/s11063-022-10868-9 |
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| Katalog-ID: |
SPR048501395 |
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| 520 | |a Abstract This paper states with the objective of investigating the finite-time stabilization and fixed-time stabilization analysis for stochastic second-order neutral-type neural networks with mixed delays. By using a variable transformation, we first rewrite the original system as a first-order differential system. By designing some feedback control laws inputs, stochastic analysis theory, finite-time stability theorem, fixed-time stability theorem, based on Lyapunov Functionals and inequalities techniques, new sufficient conditions ensuring the finite/fixed-time stabilization of the suggested system are given. Finally, the developed main control schemes, the finite/fixed-time stabilization for the stochastic Neural Networks are confirmed by two simulations examples. | ||
| 650 | 4 | |a Fixed-time stability |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Finite-time stability |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Inertial |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Stochastic perturbations |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Time-varying delays |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Jallouli, Hediene |4 aut | |
| 700 | 1 | |a Zhu, Quanxin |0 (orcid)0000-0003-3130-4923 |4 aut | |
| 700 | 1 | |a Huang, Tingwen |4 aut | |
| 700 | 1 | |a Shi, Kaibo |4 aut | |
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10.1007/s11063-022-10868-9 doi (DE-627)SPR048501395 (SPR)s11063-022-10868-9-e DE-627 ger DE-627 rakwb eng Aouiti, Chaouki verfasserin aut New Results on Finite/Fixed-Time Stabilization of Stochastic Second-Order Neutral-Type Neural Networks with Mixed Delays 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract This paper states with the objective of investigating the finite-time stabilization and fixed-time stabilization analysis for stochastic second-order neutral-type neural networks with mixed delays. By using a variable transformation, we first rewrite the original system as a first-order differential system. By designing some feedback control laws inputs, stochastic analysis theory, finite-time stability theorem, fixed-time stability theorem, based on Lyapunov Functionals and inequalities techniques, new sufficient conditions ensuring the finite/fixed-time stabilization of the suggested system are given. Finally, the developed main control schemes, the finite/fixed-time stabilization for the stochastic Neural Networks are confirmed by two simulations examples. Fixed-time stability (dpeaa)DE-He213 Finite-time stability (dpeaa)DE-He213 Inertial (dpeaa)DE-He213 Stochastic perturbations (dpeaa)DE-He213 Time-varying delays (dpeaa)DE-He213 Jallouli, Hediene aut Zhu, Quanxin (orcid)0000-0003-3130-4923 aut Huang, Tingwen aut Shi, Kaibo aut Enthalten in Neural processing letters Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 54(2022), 6 vom: 21. Mai, Seite 5415-5437 (DE-627)270932607 (DE-600)1478375-7 1573-773X nnns volume:54 year:2022 number:6 day:21 month:05 pages:5415-5437 https://dx.doi.org/10.1007/s11063-022-10868-9 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_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_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 54 2022 6 21 05 5415-5437 |
| spelling |
10.1007/s11063-022-10868-9 doi (DE-627)SPR048501395 (SPR)s11063-022-10868-9-e DE-627 ger DE-627 rakwb eng Aouiti, Chaouki verfasserin aut New Results on Finite/Fixed-Time Stabilization of Stochastic Second-Order Neutral-Type Neural Networks with Mixed Delays 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract This paper states with the objective of investigating the finite-time stabilization and fixed-time stabilization analysis for stochastic second-order neutral-type neural networks with mixed delays. By using a variable transformation, we first rewrite the original system as a first-order differential system. By designing some feedback control laws inputs, stochastic analysis theory, finite-time stability theorem, fixed-time stability theorem, based on Lyapunov Functionals and inequalities techniques, new sufficient conditions ensuring the finite/fixed-time stabilization of the suggested system are given. Finally, the developed main control schemes, the finite/fixed-time stabilization for the stochastic Neural Networks are confirmed by two simulations examples. Fixed-time stability (dpeaa)DE-He213 Finite-time stability (dpeaa)DE-He213 Inertial (dpeaa)DE-He213 Stochastic perturbations (dpeaa)DE-He213 Time-varying delays (dpeaa)DE-He213 Jallouli, Hediene aut Zhu, Quanxin (orcid)0000-0003-3130-4923 aut Huang, Tingwen aut Shi, Kaibo aut Enthalten in Neural processing letters Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 54(2022), 6 vom: 21. Mai, Seite 5415-5437 (DE-627)270932607 (DE-600)1478375-7 1573-773X nnns volume:54 year:2022 number:6 day:21 month:05 pages:5415-5437 https://dx.doi.org/10.1007/s11063-022-10868-9 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_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_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 54 2022 6 21 05 5415-5437 |
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10.1007/s11063-022-10868-9 doi (DE-627)SPR048501395 (SPR)s11063-022-10868-9-e DE-627 ger DE-627 rakwb eng Aouiti, Chaouki verfasserin aut New Results on Finite/Fixed-Time Stabilization of Stochastic Second-Order Neutral-Type Neural Networks with Mixed Delays 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract This paper states with the objective of investigating the finite-time stabilization and fixed-time stabilization analysis for stochastic second-order neutral-type neural networks with mixed delays. By using a variable transformation, we first rewrite the original system as a first-order differential system. By designing some feedback control laws inputs, stochastic analysis theory, finite-time stability theorem, fixed-time stability theorem, based on Lyapunov Functionals and inequalities techniques, new sufficient conditions ensuring the finite/fixed-time stabilization of the suggested system are given. Finally, the developed main control schemes, the finite/fixed-time stabilization for the stochastic Neural Networks are confirmed by two simulations examples. Fixed-time stability (dpeaa)DE-He213 Finite-time stability (dpeaa)DE-He213 Inertial (dpeaa)DE-He213 Stochastic perturbations (dpeaa)DE-He213 Time-varying delays (dpeaa)DE-He213 Jallouli, Hediene aut Zhu, Quanxin (orcid)0000-0003-3130-4923 aut Huang, Tingwen aut Shi, Kaibo aut Enthalten in Neural processing letters Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 54(2022), 6 vom: 21. Mai, Seite 5415-5437 (DE-627)270932607 (DE-600)1478375-7 1573-773X nnns volume:54 year:2022 number:6 day:21 month:05 pages:5415-5437 https://dx.doi.org/10.1007/s11063-022-10868-9 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_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_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 54 2022 6 21 05 5415-5437 |
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10.1007/s11063-022-10868-9 doi (DE-627)SPR048501395 (SPR)s11063-022-10868-9-e DE-627 ger DE-627 rakwb eng Aouiti, Chaouki verfasserin aut New Results on Finite/Fixed-Time Stabilization of Stochastic Second-Order Neutral-Type Neural Networks with Mixed Delays 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract This paper states with the objective of investigating the finite-time stabilization and fixed-time stabilization analysis for stochastic second-order neutral-type neural networks with mixed delays. By using a variable transformation, we first rewrite the original system as a first-order differential system. By designing some feedback control laws inputs, stochastic analysis theory, finite-time stability theorem, fixed-time stability theorem, based on Lyapunov Functionals and inequalities techniques, new sufficient conditions ensuring the finite/fixed-time stabilization of the suggested system are given. Finally, the developed main control schemes, the finite/fixed-time stabilization for the stochastic Neural Networks are confirmed by two simulations examples. Fixed-time stability (dpeaa)DE-He213 Finite-time stability (dpeaa)DE-He213 Inertial (dpeaa)DE-He213 Stochastic perturbations (dpeaa)DE-He213 Time-varying delays (dpeaa)DE-He213 Jallouli, Hediene aut Zhu, Quanxin (orcid)0000-0003-3130-4923 aut Huang, Tingwen aut Shi, Kaibo aut Enthalten in Neural processing letters Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 54(2022), 6 vom: 21. Mai, Seite 5415-5437 (DE-627)270932607 (DE-600)1478375-7 1573-773X nnns volume:54 year:2022 number:6 day:21 month:05 pages:5415-5437 https://dx.doi.org/10.1007/s11063-022-10868-9 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_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_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 54 2022 6 21 05 5415-5437 |
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10.1007/s11063-022-10868-9 doi (DE-627)SPR048501395 (SPR)s11063-022-10868-9-e DE-627 ger DE-627 rakwb eng Aouiti, Chaouki verfasserin aut New Results on Finite/Fixed-Time Stabilization of Stochastic Second-Order Neutral-Type Neural Networks with Mixed Delays 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract This paper states with the objective of investigating the finite-time stabilization and fixed-time stabilization analysis for stochastic second-order neutral-type neural networks with mixed delays. By using a variable transformation, we first rewrite the original system as a first-order differential system. By designing some feedback control laws inputs, stochastic analysis theory, finite-time stability theorem, fixed-time stability theorem, based on Lyapunov Functionals and inequalities techniques, new sufficient conditions ensuring the finite/fixed-time stabilization of the suggested system are given. Finally, the developed main control schemes, the finite/fixed-time stabilization for the stochastic Neural Networks are confirmed by two simulations examples. Fixed-time stability (dpeaa)DE-He213 Finite-time stability (dpeaa)DE-He213 Inertial (dpeaa)DE-He213 Stochastic perturbations (dpeaa)DE-He213 Time-varying delays (dpeaa)DE-He213 Jallouli, Hediene aut Zhu, Quanxin (orcid)0000-0003-3130-4923 aut Huang, Tingwen aut Shi, Kaibo aut Enthalten in Neural processing letters Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 54(2022), 6 vom: 21. Mai, Seite 5415-5437 (DE-627)270932607 (DE-600)1478375-7 1573-773X nnns volume:54 year:2022 number:6 day:21 month:05 pages:5415-5437 https://dx.doi.org/10.1007/s11063-022-10868-9 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_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_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 54 2022 6 21 05 5415-5437 |
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Aouiti, Chaouki |
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Aouiti, Chaouki misc Fixed-time stability misc Finite-time stability misc Inertial misc Stochastic perturbations misc Time-varying delays New Results on Finite/Fixed-Time Stabilization of Stochastic Second-Order Neutral-Type Neural Networks with Mixed Delays |
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New Results on Finite/Fixed-Time Stabilization of Stochastic Second-Order Neutral-Type Neural Networks with Mixed Delays Fixed-time stability (dpeaa)DE-He213 Finite-time stability (dpeaa)DE-He213 Inertial (dpeaa)DE-He213 Stochastic perturbations (dpeaa)DE-He213 Time-varying delays (dpeaa)DE-He213 |
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new results on finite/fixed-time stabilization of stochastic second-order neutral-type neural networks with mixed delays |
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New Results on Finite/Fixed-Time Stabilization of Stochastic Second-Order Neutral-Type Neural Networks with Mixed Delays |
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Abstract This paper states with the objective of investigating the finite-time stabilization and fixed-time stabilization analysis for stochastic second-order neutral-type neural networks with mixed delays. By using a variable transformation, we first rewrite the original system as a first-order differential system. By designing some feedback control laws inputs, stochastic analysis theory, finite-time stability theorem, fixed-time stability theorem, based on Lyapunov Functionals and inequalities techniques, new sufficient conditions ensuring the finite/fixed-time stabilization of the suggested system are given. Finally, the developed main control schemes, the finite/fixed-time stabilization for the stochastic Neural Networks are confirmed by two simulations examples. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
| abstractGer |
Abstract This paper states with the objective of investigating the finite-time stabilization and fixed-time stabilization analysis for stochastic second-order neutral-type neural networks with mixed delays. By using a variable transformation, we first rewrite the original system as a first-order differential system. By designing some feedback control laws inputs, stochastic analysis theory, finite-time stability theorem, fixed-time stability theorem, based on Lyapunov Functionals and inequalities techniques, new sufficient conditions ensuring the finite/fixed-time stabilization of the suggested system are given. Finally, the developed main control schemes, the finite/fixed-time stabilization for the stochastic Neural Networks are confirmed by two simulations examples. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
| abstract_unstemmed |
Abstract This paper states with the objective of investigating the finite-time stabilization and fixed-time stabilization analysis for stochastic second-order neutral-type neural networks with mixed delays. By using a variable transformation, we first rewrite the original system as a first-order differential system. By designing some feedback control laws inputs, stochastic analysis theory, finite-time stability theorem, fixed-time stability theorem, based on Lyapunov Functionals and inequalities techniques, new sufficient conditions ensuring the finite/fixed-time stabilization of the suggested system are given. Finally, the developed main control schemes, the finite/fixed-time stabilization for the stochastic Neural Networks are confirmed by two simulations examples. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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New Results on Finite/Fixed-Time Stabilization of Stochastic Second-Order Neutral-Type Neural Networks with Mixed Delays |
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https://dx.doi.org/10.1007/s11063-022-10868-9 |
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Jallouli, Hediene Zhu, Quanxin Huang, Tingwen Shi, Kaibo |
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10.1007/s11063-022-10868-9 |
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2024-07-03T19:36:56.938Z |
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