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Soft Randomized Machine Learning Procedure for Modeling Dynamic Interaction of Regional Systems

1,2,3,4
1
Federal Research Center “Computer Science and Control” of Russian Academy of Sciences, 119333 Moscow, Russia
2
Institute of Control Sciences of Russian Academy of Sciences, 117997 Moscow, Russia
3
Department of Software Engineering, ORT Braude College, 216100 Karmiel, Israel
4
Yugra Research Institute for Information Technologies, 628011 Khanty-Mansiysk, Russia
Entropy 2019, 21(4), 424; https://doi.org/10.3390/e21040424
Received: 19 March 2019 / Revised: 10 April 2019 / Accepted: 11 April 2019 / Published: 20 April 2019
(This article belongs to the Special Issue Entropy Application for Forecasting)
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Abstract

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. View Full-Text
Keywords: soft randomization; entropy; entropy operator; migration; immigration; empirical balance; empirical risk soft randomization; entropy; entropy operator; migration; immigration; empirical balance; empirical risk
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Popkov, Y.S. Soft Randomized Machine Learning Procedure for Modeling Dynamic Interaction of Regional Systems. Entropy 2019, 21, 424.

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