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A Survey of Function Analysis and Applied Dynamic Equations on Hybrid Time Scales

As an effective tool to unify discrete and continuous analysis, time scale calculus have been widely applied to study dynamic systems in both theoretical and practical aspects. In addition to such a classical role of unification, the dynamic equations on time scales have their own unique features wh...

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Detalles Bibliográficos
Autores principales: Wang, Chao, Agarwal, Ravi P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8069181/
https://www.ncbi.nlm.nih.gov/pubmed/33920402
http://dx.doi.org/10.3390/e23040450
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author Wang, Chao
Agarwal, Ravi P.
author_facet Wang, Chao
Agarwal, Ravi P.
author_sort Wang, Chao
collection PubMed
description As an effective tool to unify discrete and continuous analysis, time scale calculus have been widely applied to study dynamic systems in both theoretical and practical aspects. In addition to such a classical role of unification, the dynamic equations on time scales have their own unique features which the difference and differential equations do not possess and these advantages have been highlighted in describing some complicated dynamical behavior in the hybrid time process. In this review article, we conduct a survey of abstract analysis and applied dynamic equations on hybrid time scales, some recent main results and the related developments on hybrid time scales will be reported and the future research related to this research field is discussed. The results presented in this article can be extended and generalized to study both pure mathematical analysis and real applications such as mathematical physics, biological dynamical models and neural networks, etc.
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spelling pubmed-80691812021-04-26 A Survey of Function Analysis and Applied Dynamic Equations on Hybrid Time Scales Wang, Chao Agarwal, Ravi P. Entropy (Basel) Review As an effective tool to unify discrete and continuous analysis, time scale calculus have been widely applied to study dynamic systems in both theoretical and practical aspects. In addition to such a classical role of unification, the dynamic equations on time scales have their own unique features which the difference and differential equations do not possess and these advantages have been highlighted in describing some complicated dynamical behavior in the hybrid time process. In this review article, we conduct a survey of abstract analysis and applied dynamic equations on hybrid time scales, some recent main results and the related developments on hybrid time scales will be reported and the future research related to this research field is discussed. The results presented in this article can be extended and generalized to study both pure mathematical analysis and real applications such as mathematical physics, biological dynamical models and neural networks, etc. MDPI 2021-04-11 /pmc/articles/PMC8069181/ /pubmed/33920402 http://dx.doi.org/10.3390/e23040450 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Wang, Chao
Agarwal, Ravi P.
A Survey of Function Analysis and Applied Dynamic Equations on Hybrid Time Scales
title A Survey of Function Analysis and Applied Dynamic Equations on Hybrid Time Scales
title_full A Survey of Function Analysis and Applied Dynamic Equations on Hybrid Time Scales
title_fullStr A Survey of Function Analysis and Applied Dynamic Equations on Hybrid Time Scales
title_full_unstemmed A Survey of Function Analysis and Applied Dynamic Equations on Hybrid Time Scales
title_short A Survey of Function Analysis and Applied Dynamic Equations on Hybrid Time Scales
title_sort survey of function analysis and applied dynamic equations on hybrid time scales
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8069181/
https://www.ncbi.nlm.nih.gov/pubmed/33920402
http://dx.doi.org/10.3390/e23040450
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