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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Bibliographic Details
Published in:Entropy (Basel, Switzerland) Switzerland), 2019-04, Vol.21 (4), p.424
Main Author: Popkov, Yuri S
Format: Article
Language:English
Subjects:
Computer simulation
empirical balance
empirical risk
entropy
entropy operator
Hypotheses
Immigration
Interaction models
Lagrange multiplier
Machine learning
Migration
Noncitizens
Norms
Population
Probability density functions
Randomization
Regions
soft randomization
Variables
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Summary: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.
ISSN:1099-4300
1099-4300
DOI:10.3390/e21040424