Reconstruction and Prediction of Regional Population Migration Neural Network Model with Age Structure

Cuiying Li, Yulin Wu, Yi Cheng, Yandong Guo (), Kun Wei () and Jie Zhao
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Cuiying Li: School of Mathematical Sciences, Bohai University, Jinzhou 121013, China
Yulin Wu: School of Mathematical Sciences, Bohai University, Jinzhou 121013, China
Yi Cheng: School of Mathematical Sciences, Bohai University, Jinzhou 121013, China
Yandong Guo: School of Mathematical Sciences, Bohai University, Jinzhou 121013, China
Kun Wei: Defense Innovation Institute, Academy of Military Science, Beijing 100071, China
Jie Zhao: Defense Innovation Institute, Academy of Military Science, Beijing 100071, China

Mathematics, 2025, vol. 13, issue 5, 1-16

Abstract: The rationale for age-structured population migration system models lies in the significant impact of age patterns on migration dynamics, as age-specific migration rates exhibit distinct regularities and are influenced by life course transitions, socio-economic conditions, and demographic structures. Based on artificial neural networks, this article proposes a class of population models with age structure described by partial differential equations to predict the future trends of regional population changes. The population migration rate, as a complex nonlinear feature, can be trained through artificial neural networks, providing a population approximation system. By employing semigroup theory, we establish the well-posedness of the proposed system. It is shown that the solution of the approximation system can converge to that of the original system in the sense of the L 2 -norm. Finally, several simulation experiments are provided to verify the effectiveness of the population forecasting model.

Keywords: partial differential equation; population model; artificial neural network; well-posedness; approximation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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