Cohen-Grossberg neural networks with unpredictable and Poisson stable dynamics

In this paper, we provide theoretical as well as numerical results concerning recurrent oscillations in Cohen-Grossberg neural networks with variable inputs and strengths of connectivity for cells, which are unpredictable or Poisson stable functions. A special case of the compartmental coefficients...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Akhmet, Marat [verfasserIn] Tleubergenova, Madina [verfasserIn] Zhamanshin, Akylbek [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Cohen-Grossberg neural networks Unpredictable and Poisson stable inputs and strengths of connectivity Unpredictable and Poisson stable outputs Compartmental periodic unpredictable inputs and strengths of connectivity Exponential stability Numerical simulations

Übergeordnetes Werk:	Enthalten in: Chaos, solitons & fractals - Amsterdam [u.a.] : Elsevier Science, 1991, 178
Übergeordnetes Werk:	volume:178

DOI / URN:	10.1016/j.chaos.2023.114307

Katalog-ID:	ELV066321964

Internformat


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245	1	0	\|a Cohen-Grossberg neural networks with unpredictable and Poisson stable dynamics
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520			\|a In this paper, we provide theoretical as well as numerical results concerning recurrent oscillations in Cohen-Grossberg neural networks with variable inputs and strengths of connectivity for cells, which are unpredictable or Poisson stable functions. A special case of the compartmental coefficients with periodic and unpredictable ingredients is also carefully researched. By numerical and graphical analysis, it is shown how a constructive technical characteristic, the degree of periodicity, reflects contributions of the ingredients to final outputs of the neural networks. Sufficient conditions are obtained to guarantee the existence of exponentially stable unpredictable outputs of the models. They are specified for Poisson stability by utilizing the original method of included intervals. Examples with numerical simulations that support the theoretical results are provided.
650		4	\|a Cohen-Grossberg neural networks
650		4	\|a Unpredictable and Poisson stable inputs and strengths of connectivity
650		4	\|a Unpredictable and Poisson stable outputs
650		4	\|a Compartmental periodic unpredictable inputs and strengths of connectivity
650		4	\|a Exponential stability
650		4	\|a Numerical simulations
700	1		\|a Tleubergenova, Madina \|e verfasserin \|4 aut
700	1		\|a Zhamanshin, Akylbek \|e verfasserin \|0 (orcid)0000-0003-4878-4927 \|4 aut
773	0	8	\|i Enthalten in \|t Chaos, solitons & fractals \|d Amsterdam [u.a.] : Elsevier Science, 1991 \|g 178 \|h Online-Ressource \|w (DE-627)314118497 \|w (DE-600)2003919-0 \|w (DE-576)094504040 \|x 1873-2887 \|7 nnns
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allfields	10.1016/j.chaos.2023.114307 doi (DE-627)ELV066321964 (ELSEVIER)S0960-0779(23)01209-2 DE-627 ger DE-627 rda eng 510 VZ 30.20 bkl 31.00 bkl Akhmet, Marat verfasserin (orcid)0000-0002-2985-286X aut Cohen-Grossberg neural networks with unpredictable and Poisson stable dynamics 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, we provide theoretical as well as numerical results concerning recurrent oscillations in Cohen-Grossberg neural networks with variable inputs and strengths of connectivity for cells, which are unpredictable or Poisson stable functions. A special case of the compartmental coefficients with periodic and unpredictable ingredients is also carefully researched. By numerical and graphical analysis, it is shown how a constructive technical characteristic, the degree of periodicity, reflects contributions of the ingredients to final outputs of the neural networks. Sufficient conditions are obtained to guarantee the existence of exponentially stable unpredictable outputs of the models. They are specified for Poisson stability by utilizing the original method of included intervals. Examples with numerical simulations that support the theoretical results are provided. Cohen-Grossberg neural networks Unpredictable and Poisson stable inputs and strengths of connectivity Unpredictable and Poisson stable outputs Compartmental periodic unpredictable inputs and strengths of connectivity Exponential stability Numerical simulations Tleubergenova, Madina verfasserin aut Zhamanshin, Akylbek verfasserin (orcid)0000-0003-4878-4927 aut Enthalten in Chaos, solitons & fractals Amsterdam [u.a.] : Elsevier Science, 1991 178 Online-Ressource (DE-627)314118497 (DE-600)2003919-0 (DE-576)094504040 1873-2887 nnns volume:178 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 30.20 Nichtlineare Dynamik VZ 31.00 Mathematik: Allgemeines VZ AR 178
spelling	10.1016/j.chaos.2023.114307 doi (DE-627)ELV066321964 (ELSEVIER)S0960-0779(23)01209-2 DE-627 ger DE-627 rda eng 510 VZ 30.20 bkl 31.00 bkl Akhmet, Marat verfasserin (orcid)0000-0002-2985-286X aut Cohen-Grossberg neural networks with unpredictable and Poisson stable dynamics 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, we provide theoretical as well as numerical results concerning recurrent oscillations in Cohen-Grossberg neural networks with variable inputs and strengths of connectivity for cells, which are unpredictable or Poisson stable functions. A special case of the compartmental coefficients with periodic and unpredictable ingredients is also carefully researched. By numerical and graphical analysis, it is shown how a constructive technical characteristic, the degree of periodicity, reflects contributions of the ingredients to final outputs of the neural networks. Sufficient conditions are obtained to guarantee the existence of exponentially stable unpredictable outputs of the models. They are specified for Poisson stability by utilizing the original method of included intervals. Examples with numerical simulations that support the theoretical results are provided. Cohen-Grossberg neural networks Unpredictable and Poisson stable inputs and strengths of connectivity Unpredictable and Poisson stable outputs Compartmental periodic unpredictable inputs and strengths of connectivity Exponential stability Numerical simulations Tleubergenova, Madina verfasserin aut Zhamanshin, Akylbek verfasserin (orcid)0000-0003-4878-4927 aut Enthalten in Chaos, solitons & fractals Amsterdam [u.a.] : Elsevier Science, 1991 178 Online-Ressource (DE-627)314118497 (DE-600)2003919-0 (DE-576)094504040 1873-2887 nnns volume:178 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 30.20 Nichtlineare Dynamik VZ 31.00 Mathematik: Allgemeines VZ AR 178
allfields_unstemmed	10.1016/j.chaos.2023.114307 doi (DE-627)ELV066321964 (ELSEVIER)S0960-0779(23)01209-2 DE-627 ger DE-627 rda eng 510 VZ 30.20 bkl 31.00 bkl Akhmet, Marat verfasserin (orcid)0000-0002-2985-286X aut Cohen-Grossberg neural networks with unpredictable and Poisson stable dynamics 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, we provide theoretical as well as numerical results concerning recurrent oscillations in Cohen-Grossberg neural networks with variable inputs and strengths of connectivity for cells, which are unpredictable or Poisson stable functions. A special case of the compartmental coefficients with periodic and unpredictable ingredients is also carefully researched. By numerical and graphical analysis, it is shown how a constructive technical characteristic, the degree of periodicity, reflects contributions of the ingredients to final outputs of the neural networks. Sufficient conditions are obtained to guarantee the existence of exponentially stable unpredictable outputs of the models. They are specified for Poisson stability by utilizing the original method of included intervals. Examples with numerical simulations that support the theoretical results are provided. Cohen-Grossberg neural networks Unpredictable and Poisson stable inputs and strengths of connectivity Unpredictable and Poisson stable outputs Compartmental periodic unpredictable inputs and strengths of connectivity Exponential stability Numerical simulations Tleubergenova, Madina verfasserin aut Zhamanshin, Akylbek verfasserin (orcid)0000-0003-4878-4927 aut Enthalten in Chaos, solitons & fractals Amsterdam [u.a.] : Elsevier Science, 1991 178 Online-Ressource (DE-627)314118497 (DE-600)2003919-0 (DE-576)094504040 1873-2887 nnns volume:178 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 30.20 Nichtlineare Dynamik VZ 31.00 Mathematik: Allgemeines VZ AR 178
allfieldsGer	10.1016/j.chaos.2023.114307 doi (DE-627)ELV066321964 (ELSEVIER)S0960-0779(23)01209-2 DE-627 ger DE-627 rda eng 510 VZ 30.20 bkl 31.00 bkl Akhmet, Marat verfasserin (orcid)0000-0002-2985-286X aut Cohen-Grossberg neural networks with unpredictable and Poisson stable dynamics 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, we provide theoretical as well as numerical results concerning recurrent oscillations in Cohen-Grossberg neural networks with variable inputs and strengths of connectivity for cells, which are unpredictable or Poisson stable functions. A special case of the compartmental coefficients with periodic and unpredictable ingredients is also carefully researched. By numerical and graphical analysis, it is shown how a constructive technical characteristic, the degree of periodicity, reflects contributions of the ingredients to final outputs of the neural networks. Sufficient conditions are obtained to guarantee the existence of exponentially stable unpredictable outputs of the models. They are specified for Poisson stability by utilizing the original method of included intervals. Examples with numerical simulations that support the theoretical results are provided. Cohen-Grossberg neural networks Unpredictable and Poisson stable inputs and strengths of connectivity Unpredictable and Poisson stable outputs Compartmental periodic unpredictable inputs and strengths of connectivity Exponential stability Numerical simulations Tleubergenova, Madina verfasserin aut Zhamanshin, Akylbek verfasserin (orcid)0000-0003-4878-4927 aut Enthalten in Chaos, solitons & fractals Amsterdam [u.a.] : Elsevier Science, 1991 178 Online-Ressource (DE-627)314118497 (DE-600)2003919-0 (DE-576)094504040 1873-2887 nnns volume:178 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 30.20 Nichtlineare Dynamik VZ 31.00 Mathematik: Allgemeines VZ AR 178
allfieldsSound	10.1016/j.chaos.2023.114307 doi (DE-627)ELV066321964 (ELSEVIER)S0960-0779(23)01209-2 DE-627 ger DE-627 rda eng 510 VZ 30.20 bkl 31.00 bkl Akhmet, Marat verfasserin (orcid)0000-0002-2985-286X aut Cohen-Grossberg neural networks with unpredictable and Poisson stable dynamics 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, we provide theoretical as well as numerical results concerning recurrent oscillations in Cohen-Grossberg neural networks with variable inputs and strengths of connectivity for cells, which are unpredictable or Poisson stable functions. A special case of the compartmental coefficients with periodic and unpredictable ingredients is also carefully researched. By numerical and graphical analysis, it is shown how a constructive technical characteristic, the degree of periodicity, reflects contributions of the ingredients to final outputs of the neural networks. Sufficient conditions are obtained to guarantee the existence of exponentially stable unpredictable outputs of the models. They are specified for Poisson stability by utilizing the original method of included intervals. Examples with numerical simulations that support the theoretical results are provided. Cohen-Grossberg neural networks Unpredictable and Poisson stable inputs and strengths of connectivity Unpredictable and Poisson stable outputs Compartmental periodic unpredictable inputs and strengths of connectivity Exponential stability Numerical simulations Tleubergenova, Madina verfasserin aut Zhamanshin, Akylbek verfasserin (orcid)0000-0003-4878-4927 aut Enthalten in Chaos, solitons & fractals Amsterdam [u.a.] : Elsevier Science, 1991 178 Online-Ressource (DE-627)314118497 (DE-600)2003919-0 (DE-576)094504040 1873-2887 nnns volume:178 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 30.20 Nichtlineare Dynamik VZ 31.00 Mathematik: Allgemeines VZ AR 178
language	English
source	Enthalten in Chaos, solitons & fractals 178 volume:178
sourceStr	Enthalten in Chaos, solitons & fractals 178 volume:178
format_phy_str_mv	Article
bklname	Nichtlineare Dynamik Mathematik: Allgemeines
institution	findex.gbv.de
topic_facet	Cohen-Grossberg neural networks Unpredictable and Poisson stable inputs and strengths of connectivity Unpredictable and Poisson stable outputs Compartmental periodic unpredictable inputs and strengths of connectivity Exponential stability Numerical simulations
dewey-raw	510
isfreeaccess_bool	false
container_title	Chaos, solitons & fractals
authorswithroles_txt_mv	Akhmet, Marat @@aut@@ Tleubergenova, Madina @@aut@@ Zhamanshin, Akylbek @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	314118497
dewey-sort	3510
id	ELV066321964
language_de	englisch
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author	Akhmet, Marat
spellingShingle	Akhmet, Marat ddc 510 bkl 30.20 bkl 31.00 misc Cohen-Grossberg neural networks misc Unpredictable and Poisson stable inputs and strengths of connectivity misc Unpredictable and Poisson stable outputs misc Compartmental periodic unpredictable inputs and strengths of connectivity misc Exponential stability misc Numerical simulations Cohen-Grossberg neural networks with unpredictable and Poisson stable dynamics
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topic_title	510 VZ 30.20 bkl 31.00 bkl Cohen-Grossberg neural networks with unpredictable and Poisson stable dynamics Cohen-Grossberg neural networks Unpredictable and Poisson stable inputs and strengths of connectivity Unpredictable and Poisson stable outputs Compartmental periodic unpredictable inputs and strengths of connectivity Exponential stability Numerical simulations
topic	ddc 510 bkl 30.20 bkl 31.00 misc Cohen-Grossberg neural networks misc Unpredictable and Poisson stable inputs and strengths of connectivity misc Unpredictable and Poisson stable outputs misc Compartmental periodic unpredictable inputs and strengths of connectivity misc Exponential stability misc Numerical simulations
topic_unstemmed	ddc 510 bkl 30.20 bkl 31.00 misc Cohen-Grossberg neural networks misc Unpredictable and Poisson stable inputs and strengths of connectivity misc Unpredictable and Poisson stable outputs misc Compartmental periodic unpredictable inputs and strengths of connectivity misc Exponential stability misc Numerical simulations
topic_browse	ddc 510 bkl 30.20 bkl 31.00 misc Cohen-Grossberg neural networks misc Unpredictable and Poisson stable inputs and strengths of connectivity misc Unpredictable and Poisson stable outputs misc Compartmental periodic unpredictable inputs and strengths of connectivity misc Exponential stability misc Numerical simulations
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title	Cohen-Grossberg neural networks with unpredictable and Poisson stable dynamics
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title_full	Cohen-Grossberg neural networks with unpredictable and Poisson stable dynamics
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author_browse	Akhmet, Marat Tleubergenova, Madina Zhamanshin, Akylbek
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title_sort	cohen-grossberg neural networks with unpredictable and poisson stable dynamics
title_auth	Cohen-Grossberg neural networks with unpredictable and Poisson stable dynamics
abstract	In this paper, we provide theoretical as well as numerical results concerning recurrent oscillations in Cohen-Grossberg neural networks with variable inputs and strengths of connectivity for cells, which are unpredictable or Poisson stable functions. A special case of the compartmental coefficients with periodic and unpredictable ingredients is also carefully researched. By numerical and graphical analysis, it is shown how a constructive technical characteristic, the degree of periodicity, reflects contributions of the ingredients to final outputs of the neural networks. Sufficient conditions are obtained to guarantee the existence of exponentially stable unpredictable outputs of the models. They are specified for Poisson stability by utilizing the original method of included intervals. Examples with numerical simulations that support the theoretical results are provided.
abstractGer	In this paper, we provide theoretical as well as numerical results concerning recurrent oscillations in Cohen-Grossberg neural networks with variable inputs and strengths of connectivity for cells, which are unpredictable or Poisson stable functions. A special case of the compartmental coefficients with periodic and unpredictable ingredients is also carefully researched. By numerical and graphical analysis, it is shown how a constructive technical characteristic, the degree of periodicity, reflects contributions of the ingredients to final outputs of the neural networks. Sufficient conditions are obtained to guarantee the existence of exponentially stable unpredictable outputs of the models. They are specified for Poisson stability by utilizing the original method of included intervals. Examples with numerical simulations that support the theoretical results are provided.
abstract_unstemmed	In this paper, we provide theoretical as well as numerical results concerning recurrent oscillations in Cohen-Grossberg neural networks with variable inputs and strengths of connectivity for cells, which are unpredictable or Poisson stable functions. A special case of the compartmental coefficients with periodic and unpredictable ingredients is also carefully researched. By numerical and graphical analysis, it is shown how a constructive technical characteristic, the degree of periodicity, reflects contributions of the ingredients to final outputs of the neural networks. Sufficient conditions are obtained to guarantee the existence of exponentially stable unpredictable outputs of the models. They are specified for Poisson stability by utilizing the original method of included intervals. Examples with numerical simulations that support the theoretical results are provided.
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title_short	Cohen-Grossberg neural networks with unpredictable and Poisson stable dynamics
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up_date	2024-07-06T17:20:51.001Z
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Cohen-Grossberg neural networks with unpredictable and Poisson stable dynamics

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