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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-04-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/21/4/424 |
| _version_ | 1811299473378246656 |
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| author | Yuri S. Popkov |
| author_facet | Yuri S. Popkov |
| author_sort | Yuri S. Popkov |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-13T06:36:29Z |
| format | Article |
| id | doaj.art-5b5a972fbf9c46579ecf09031f1b0fa5 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-13T06:36:29Z |
| publishDate | 2019-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-5b5a972fbf9c46579ecf09031f1b0fa52022-12-22T02:57:53ZengMDPI AGEntropy1099-43002019-04-0121442410.3390/e21040424e21040424Soft Randomized Machine Learning Procedure for Modeling Dynamic Interaction of Regional SystemsYuri S. Popkov0Federal Research Center “Computer Science and Control” of Russian Academy of Sciences, 119333 Moscow, RussiaThe 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.https://www.mdpi.com/1099-4300/21/4/424soft randomizationentropyentropy operatormigrationimmigrationempirical balanceempirical risk |
| spellingShingle | Yuri S. Popkov Soft Randomized Machine Learning Procedure for Modeling Dynamic Interaction of Regional Systems Entropy soft randomization entropy entropy operator migration immigration empirical balance empirical risk |
| 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 | soft randomization entropy entropy operator migration immigration empirical balance empirical risk |
| url | https://www.mdpi.com/1099-4300/21/4/424 |
| work_keys_str_mv | AT yurispopkov softrandomizedmachinelearningprocedureformodelingdynamicinteractionofregionalsystems |