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Population prediction in China based on maximum information coefficient and NAR-BP neural network

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DOI: 10.23977/jeis.2023.080506 | Downloads: 15 | Views: 409

Author(s)

Fei Zeng 1

Affiliation(s)

1 School of Mathematics and Computer Science, Gannan Normal University, Ganzhou, 341000, China

Corresponding Author

Fei Zeng

ABSTRACT

This paper studies the issue of national population census. Firstly, the paper collects census related data, establish a maximum information coefficient model, and preprocess the data. Then, it establishes a dynamic neural network prediction model based on NAR-BP to predict the total population of China in 2030. Furthermore, a PCA based NAR-BP dynamic neural network prediction model was established to predict the proportion of males and urban population in China by 2030. Finally, a neural network optimization model based on GA-BP was established to obtain the optimal search term. Based on the analysis of experimental results, it is proven that the frequency of chinese population census is appropriate to be a cycle of 10 years.

KEYWORDS

NAR-BP Dynamic Neural Network, Primary Constitution Analysis Model, GA-BP neural network, Census

CITE THIS PAPER

Fei Zeng, Population prediction in China based on maximum information coefficient and NAR-BP neural network. Journal of Electronics and Information Science (2023) Vol. 8: 38-44. DOI: http://dx.doi.org/10.23977/10.23977/jeis.2023.080506.

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