Cargando…
Soft Randomized Machine Learning Procedure for Modeling Dynamic Interaction of Regional Systems
The paper suggests a randomized model for dynamic migratory interaction of regional systems. The locally stationary states of migration flows in the basic and immigration systems are described by corresponding entropy operators. A soft randomization procedure that defines the optimal probability den...
Autor principal: | |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514913/ https://www.ncbi.nlm.nih.gov/pubmed/33267138 http://dx.doi.org/10.3390/e21040424 |
_version_ | 1783586697080995840 |
---|---|
author | Popkov, Yuri S. |
author_facet | Popkov, Yuri S. |
author_sort | Popkov, Yuri S. |
collection | PubMed |
description | The paper suggests a randomized model for dynamic migratory interaction of regional systems. The locally stationary states of migration flows in the basic and immigration systems are described by corresponding entropy operators. A soft randomization procedure that defines the optimal probability density functions of system parameters and measurement noises is developed. The advantages of soft randomization with approximate empirical data balance conditions are demonstrated, which considerably reduces algorithmic complexity and computational resources demand. An example of migratory interaction modeling and testing is given. |
format | Online Article Text |
id | pubmed-7514913 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75149132020-11-09 Soft Randomized Machine Learning Procedure for Modeling Dynamic Interaction of Regional Systems Popkov, Yuri S. Entropy (Basel) Article The paper suggests a randomized model for dynamic migratory interaction of regional systems. The locally stationary states of migration flows in the basic and immigration systems are described by corresponding entropy operators. A soft randomization procedure that defines the optimal probability density functions of system parameters and measurement noises is developed. The advantages of soft randomization with approximate empirical data balance conditions are demonstrated, which considerably reduces algorithmic complexity and computational resources demand. An example of migratory interaction modeling and testing is given. MDPI 2019-04-20 /pmc/articles/PMC7514913/ /pubmed/33267138 http://dx.doi.org/10.3390/e21040424 Text en © 2019 by the author. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Popkov, Yuri S. Soft Randomized Machine Learning Procedure for Modeling Dynamic Interaction of Regional Systems |
title | Soft Randomized Machine Learning Procedure for Modeling Dynamic Interaction of Regional Systems |
title_full | Soft Randomized Machine Learning Procedure for Modeling Dynamic Interaction of Regional Systems |
title_fullStr | Soft Randomized Machine Learning Procedure for Modeling Dynamic Interaction of Regional Systems |
title_full_unstemmed | Soft Randomized Machine Learning Procedure for Modeling Dynamic Interaction of Regional Systems |
title_short | Soft Randomized Machine Learning Procedure for Modeling Dynamic Interaction of Regional Systems |
title_sort | soft randomized machine learning procedure for modeling dynamic interaction of regional systems |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514913/ https://www.ncbi.nlm.nih.gov/pubmed/33267138 http://dx.doi.org/10.3390/e21040424 |
work_keys_str_mv | AT popkovyuris softrandomizedmachinelearningprocedureformodelingdynamicinteractionofregionalsystems |