Finite-Time Synchronization for T–S Fuzzy Complex-Valued Inertial Delayed Neural Networks Via Decomposition Approach

Abstract This paper is mainly dedicated to the issue of finite-time synchronization of T–S fuzzy complex-valued neural networks with time-varying delays and inertial terms via directly constructing Lyapunov functions with separating the original complex-valued neural networks into two real-valued su...
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Autor*in:	Ramajayam, S. [verfasserIn] Rajavel, S. Samidurai, R. Cao, Yang

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Complex-valued neural networks (CVNNs) Inertial neural networks T–S fuzzy Finite-time Synchronization

Anmerkung:	© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Neural processing letters - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994, 55(2023), 5 vom: 09. Jan., Seite 5885-5903
Übergeordnetes Werk:	volume:55 ; year:2023 ; number:5 ; day:09 ; month:01 ; pages:5885-5903

Links:	Volltext

DOI / URN:	10.1007/s11063-022-11117-9

Katalog-ID:	SPR053249380

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520			\|a Abstract This paper is mainly dedicated to the issue of finite-time synchronization of T–S fuzzy complex-valued neural networks with time-varying delays and inertial terms via directly constructing Lyapunov functions with separating the original complex-valued neural networks into two real-valued subsystems equivalently. First of all, to facilitate the analysis of the second-order derivative caused by the inertial term, two intermediate variables are introduced to transfer complex-valued inertial delayed neural networks (CVIDNNs) into the first-order differential equation form. Next, CVIDNNs are developed using T–S fuzzy rules. By using the Lyapunov stability theory, inequality scaling skills and adjustable algebraic criteria for T–S fuzzy CVIDNNs as well as the upper bound of the settling time for synchronization, are derived. Finally, one numerical example with simulations is given to illustrate the effectiveness of our theoretical results.
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allfields	10.1007/s11063-022-11117-9 doi (DE-627)SPR053249380 (SPR)s11063-022-11117-9-e DE-627 ger DE-627 rakwb eng Ramajayam, S. verfasserin aut Finite-Time Synchronization for T–S Fuzzy Complex-Valued Inertial Delayed Neural Networks Via Decomposition Approach 2023 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 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This paper is mainly dedicated to the issue of finite-time synchronization of T–S fuzzy complex-valued neural networks with time-varying delays and inertial terms via directly constructing Lyapunov functions with separating the original complex-valued neural networks into two real-valued subsystems equivalently. First of all, to facilitate the analysis of the second-order derivative caused by the inertial term, two intermediate variables are introduced to transfer complex-valued inertial delayed neural networks (CVIDNNs) into the first-order differential equation form. Next, CVIDNNs are developed using T–S fuzzy rules. By using the Lyapunov stability theory, inequality scaling skills and adjustable algebraic criteria for T–S fuzzy CVIDNNs as well as the upper bound of the settling time for synchronization, are derived. Finally, one numerical example with simulations is given to illustrate the effectiveness of our theoretical results. Complex-valued neural networks (CVNNs) (dpeaa)DE-He213 Inertial neural networks (dpeaa)DE-He213 T–S fuzzy (dpeaa)DE-He213 Finite-time Synchronization (dpeaa)DE-He213 Rajavel, S. aut Samidurai, R. aut Cao, Yang (orcid)0000-0002-6940-0868 aut Enthalten in Neural processing letters Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 55(2023), 5 vom: 09. Jan., Seite 5885-5903 (DE-627)270932607 (DE-600)1478375-7 1573-773X nnns volume:55 year:2023 number:5 day:09 month:01 pages:5885-5903 https://dx.doi.org/10.1007/s11063-022-11117-9 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 55 2023 5 09 01 5885-5903
spelling	10.1007/s11063-022-11117-9 doi (DE-627)SPR053249380 (SPR)s11063-022-11117-9-e DE-627 ger DE-627 rakwb eng Ramajayam, S. verfasserin aut Finite-Time Synchronization for T–S Fuzzy Complex-Valued Inertial Delayed Neural Networks Via Decomposition Approach 2023 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 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This paper is mainly dedicated to the issue of finite-time synchronization of T–S fuzzy complex-valued neural networks with time-varying delays and inertial terms via directly constructing Lyapunov functions with separating the original complex-valued neural networks into two real-valued subsystems equivalently. First of all, to facilitate the analysis of the second-order derivative caused by the inertial term, two intermediate variables are introduced to transfer complex-valued inertial delayed neural networks (CVIDNNs) into the first-order differential equation form. Next, CVIDNNs are developed using T–S fuzzy rules. By using the Lyapunov stability theory, inequality scaling skills and adjustable algebraic criteria for T–S fuzzy CVIDNNs as well as the upper bound of the settling time for synchronization, are derived. Finally, one numerical example with simulations is given to illustrate the effectiveness of our theoretical results. Complex-valued neural networks (CVNNs) (dpeaa)DE-He213 Inertial neural networks (dpeaa)DE-He213 T–S fuzzy (dpeaa)DE-He213 Finite-time Synchronization (dpeaa)DE-He213 Rajavel, S. aut Samidurai, R. aut Cao, Yang (orcid)0000-0002-6940-0868 aut Enthalten in Neural processing letters Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 55(2023), 5 vom: 09. Jan., Seite 5885-5903 (DE-627)270932607 (DE-600)1478375-7 1573-773X nnns volume:55 year:2023 number:5 day:09 month:01 pages:5885-5903 https://dx.doi.org/10.1007/s11063-022-11117-9 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 55 2023 5 09 01 5885-5903
allfields_unstemmed	10.1007/s11063-022-11117-9 doi (DE-627)SPR053249380 (SPR)s11063-022-11117-9-e DE-627 ger DE-627 rakwb eng Ramajayam, S. verfasserin aut Finite-Time Synchronization for T–S Fuzzy Complex-Valued Inertial Delayed Neural Networks Via Decomposition Approach 2023 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 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This paper is mainly dedicated to the issue of finite-time synchronization of T–S fuzzy complex-valued neural networks with time-varying delays and inertial terms via directly constructing Lyapunov functions with separating the original complex-valued neural networks into two real-valued subsystems equivalently. First of all, to facilitate the analysis of the second-order derivative caused by the inertial term, two intermediate variables are introduced to transfer complex-valued inertial delayed neural networks (CVIDNNs) into the first-order differential equation form. Next, CVIDNNs are developed using T–S fuzzy rules. By using the Lyapunov stability theory, inequality scaling skills and adjustable algebraic criteria for T–S fuzzy CVIDNNs as well as the upper bound of the settling time for synchronization, are derived. Finally, one numerical example with simulations is given to illustrate the effectiveness of our theoretical results. Complex-valued neural networks (CVNNs) (dpeaa)DE-He213 Inertial neural networks (dpeaa)DE-He213 T–S fuzzy (dpeaa)DE-He213 Finite-time Synchronization (dpeaa)DE-He213 Rajavel, S. aut Samidurai, R. aut Cao, Yang (orcid)0000-0002-6940-0868 aut Enthalten in Neural processing letters Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 55(2023), 5 vom: 09. Jan., Seite 5885-5903 (DE-627)270932607 (DE-600)1478375-7 1573-773X nnns volume:55 year:2023 number:5 day:09 month:01 pages:5885-5903 https://dx.doi.org/10.1007/s11063-022-11117-9 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 55 2023 5 09 01 5885-5903
allfieldsGer	10.1007/s11063-022-11117-9 doi (DE-627)SPR053249380 (SPR)s11063-022-11117-9-e DE-627 ger DE-627 rakwb eng Ramajayam, S. verfasserin aut Finite-Time Synchronization for T–S Fuzzy Complex-Valued Inertial Delayed Neural Networks Via Decomposition Approach 2023 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 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This paper is mainly dedicated to the issue of finite-time synchronization of T–S fuzzy complex-valued neural networks with time-varying delays and inertial terms via directly constructing Lyapunov functions with separating the original complex-valued neural networks into two real-valued subsystems equivalently. First of all, to facilitate the analysis of the second-order derivative caused by the inertial term, two intermediate variables are introduced to transfer complex-valued inertial delayed neural networks (CVIDNNs) into the first-order differential equation form. Next, CVIDNNs are developed using T–S fuzzy rules. By using the Lyapunov stability theory, inequality scaling skills and adjustable algebraic criteria for T–S fuzzy CVIDNNs as well as the upper bound of the settling time for synchronization, are derived. Finally, one numerical example with simulations is given to illustrate the effectiveness of our theoretical results. Complex-valued neural networks (CVNNs) (dpeaa)DE-He213 Inertial neural networks (dpeaa)DE-He213 T–S fuzzy (dpeaa)DE-He213 Finite-time Synchronization (dpeaa)DE-He213 Rajavel, S. aut Samidurai, R. aut Cao, Yang (orcid)0000-0002-6940-0868 aut Enthalten in Neural processing letters Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 55(2023), 5 vom: 09. Jan., Seite 5885-5903 (DE-627)270932607 (DE-600)1478375-7 1573-773X nnns volume:55 year:2023 number:5 day:09 month:01 pages:5885-5903 https://dx.doi.org/10.1007/s11063-022-11117-9 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 55 2023 5 09 01 5885-5903
allfieldsSound	10.1007/s11063-022-11117-9 doi (DE-627)SPR053249380 (SPR)s11063-022-11117-9-e DE-627 ger DE-627 rakwb eng Ramajayam, S. verfasserin aut Finite-Time Synchronization for T–S Fuzzy Complex-Valued Inertial Delayed Neural Networks Via Decomposition Approach 2023 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 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This paper is mainly dedicated to the issue of finite-time synchronization of T–S fuzzy complex-valued neural networks with time-varying delays and inertial terms via directly constructing Lyapunov functions with separating the original complex-valued neural networks into two real-valued subsystems equivalently. First of all, to facilitate the analysis of the second-order derivative caused by the inertial term, two intermediate variables are introduced to transfer complex-valued inertial delayed neural networks (CVIDNNs) into the first-order differential equation form. Next, CVIDNNs are developed using T–S fuzzy rules. By using the Lyapunov stability theory, inequality scaling skills and adjustable algebraic criteria for T–S fuzzy CVIDNNs as well as the upper bound of the settling time for synchronization, are derived. Finally, one numerical example with simulations is given to illustrate the effectiveness of our theoretical results. Complex-valued neural networks (CVNNs) (dpeaa)DE-He213 Inertial neural networks (dpeaa)DE-He213 T–S fuzzy (dpeaa)DE-He213 Finite-time Synchronization (dpeaa)DE-He213 Rajavel, S. aut Samidurai, R. aut Cao, Yang (orcid)0000-0002-6940-0868 aut Enthalten in Neural processing letters Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 55(2023), 5 vom: 09. Jan., Seite 5885-5903 (DE-627)270932607 (DE-600)1478375-7 1573-773X nnns volume:55 year:2023 number:5 day:09 month:01 pages:5885-5903 https://dx.doi.org/10.1007/s11063-022-11117-9 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 55 2023 5 09 01 5885-5903
language	English
source	Enthalten in Neural processing letters 55(2023), 5 vom: 09. Jan., Seite 5885-5903 volume:55 year:2023 number:5 day:09 month:01 pages:5885-5903
sourceStr	Enthalten in Neural processing letters 55(2023), 5 vom: 09. Jan., Seite 5885-5903 volume:55 year:2023 number:5 day:09 month:01 pages:5885-5903
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Complex-valued neural networks (CVNNs) Inertial neural networks T–S fuzzy Finite-time Synchronization
isfreeaccess_bool	false
container_title	Neural processing letters
authorswithroles_txt_mv	Ramajayam, S. @@aut@@ Rajavel, S. @@aut@@ Samidurai, R. @@aut@@ Cao, Yang @@aut@@
publishDateDaySort_date	2023-01-09T00:00:00Z
hierarchy_top_id	270932607
id	SPR053249380
language_de	englisch
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author	Ramajayam, S.
spellingShingle	Ramajayam, S. misc Complex-valued neural networks (CVNNs) misc Inertial neural networks misc T–S fuzzy misc Finite-time Synchronization Finite-Time Synchronization for T–S Fuzzy Complex-Valued Inertial Delayed Neural Networks Via Decomposition Approach
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topic_title	Finite-Time Synchronization for T–S Fuzzy Complex-Valued Inertial Delayed Neural Networks Via Decomposition Approach Complex-valued neural networks (CVNNs) (dpeaa)DE-He213 Inertial neural networks (dpeaa)DE-He213 T–S fuzzy (dpeaa)DE-He213 Finite-time Synchronization (dpeaa)DE-He213
topic	misc Complex-valued neural networks (CVNNs) misc Inertial neural networks misc T–S fuzzy misc Finite-time Synchronization
topic_unstemmed	misc Complex-valued neural networks (CVNNs) misc Inertial neural networks misc T–S fuzzy misc Finite-time Synchronization
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title	Finite-Time Synchronization for T–S Fuzzy Complex-Valued Inertial Delayed Neural Networks Via Decomposition Approach
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title_full	Finite-Time Synchronization for T–S Fuzzy Complex-Valued Inertial Delayed Neural Networks Via Decomposition Approach
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title_sort	finite-time synchronization for t–s fuzzy complex-valued inertial delayed neural networks via decomposition approach
title_auth	Finite-Time Synchronization for T–S Fuzzy Complex-Valued Inertial Delayed Neural Networks Via Decomposition Approach
abstract	Abstract This paper is mainly dedicated to the issue of finite-time synchronization of T–S fuzzy complex-valued neural networks with time-varying delays and inertial terms via directly constructing Lyapunov functions with separating the original complex-valued neural networks into two real-valued subsystems equivalently. First of all, to facilitate the analysis of the second-order derivative caused by the inertial term, two intermediate variables are introduced to transfer complex-valued inertial delayed neural networks (CVIDNNs) into the first-order differential equation form. Next, CVIDNNs are developed using T–S fuzzy rules. By using the Lyapunov stability theory, inequality scaling skills and adjustable algebraic criteria for T–S fuzzy CVIDNNs as well as the upper bound of the settling time for synchronization, are derived. Finally, one numerical example with simulations is given to illustrate the effectiveness of our theoretical results. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract This paper is mainly dedicated to the issue of finite-time synchronization of T–S fuzzy complex-valued neural networks with time-varying delays and inertial terms via directly constructing Lyapunov functions with separating the original complex-valued neural networks into two real-valued subsystems equivalently. First of all, to facilitate the analysis of the second-order derivative caused by the inertial term, two intermediate variables are introduced to transfer complex-valued inertial delayed neural networks (CVIDNNs) into the first-order differential equation form. Next, CVIDNNs are developed using T–S fuzzy rules. By using the Lyapunov stability theory, inequality scaling skills and adjustable algebraic criteria for T–S fuzzy CVIDNNs as well as the upper bound of the settling time for synchronization, are derived. Finally, one numerical example with simulations is given to illustrate the effectiveness of our theoretical results. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract This paper is mainly dedicated to the issue of finite-time synchronization of T–S fuzzy complex-valued neural networks with time-varying delays and inertial terms via directly constructing Lyapunov functions with separating the original complex-valued neural networks into two real-valued subsystems equivalently. First of all, to facilitate the analysis of the second-order derivative caused by the inertial term, two intermediate variables are introduced to transfer complex-valued inertial delayed neural networks (CVIDNNs) into the first-order differential equation form. Next, CVIDNNs are developed using T–S fuzzy rules. By using the Lyapunov stability theory, inequality scaling skills and adjustable algebraic criteria for T–S fuzzy CVIDNNs as well as the upper bound of the settling time for synchronization, are derived. Finally, one numerical example with simulations is given to illustrate the effectiveness of our theoretical results. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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title_short	Finite-Time Synchronization for T–S Fuzzy Complex-Valued Inertial Delayed Neural Networks Via Decomposition Approach
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Finite-Time Synchronization for T–S Fuzzy Complex-Valued Inertial Delayed Neural Networks Via Decomposition Approach

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