%0 Journal Article
%T 基于熵权组合及生育政策下Leslie模型的山西省人口情况分析<br>Analysis of Population Situation in Shanxi Province Based on the Combination of Entropy Weight and the Leslie Model under the Fertility Policy
%A 陆星龙
%A 范振宙
%A 王菲
%J Advances in Applied Mathematics
%P 2113-2133
%@ 2324-8009
%D 2022
%I Hans Publishing
%R 10.12677/AAM.2022.114229
%X 本文从山西人口的实际情况、增长以及流动特点出发，对于短期人口建立Logistic阻滞增长模型、灰色预测模型、时间序列模型，并采用熵权法分配比重，提高预测的精准度；对于长期人口建立Leslie模型。同时建立BP神经网络模型，在无三孩生育政策影响的条件下，分别对中短期以及长期人口趋势进行了预测和分析。并利用上述的Leslie模型分别分析在无三孩和有三孩生育政策条件下的中短期以及长期的人口趋势，得出在2050年无三孩政策与有三孩政策两种情况的人口预测分别为3424.9万人和3439万人。最后我们采用灰色预测法，利用关联分析原则，对山西省人口流失进行分析和预测，得出未来十年人口一直处于流失状态但流失人口会逐渐减少。<br />
Based on the actual situation, growth and flow characteristics of Shanxi’s population, this paper establishes a Logistic retarded growth model, a gray prediction model, and a time series model for the short-term population, and the entropy weight method is used to allocate the proportion to improve the prediction accuracy; for the long-term population, a Leslie model is established. At the same time, a BP neural network model was established to predict and analyze the short-term and long-term population trends without the influence of the three-child birth policy. And using the above Leslie model to analyze the short- and medium-term and long-term population trends under the conditions of no three-child policy and three-child birth policy, it is concluded that in 2050, the population forecast for the no-three-child policy and the three-child policy is 3424.9 million and 34.39 million respectively. Finally, we use the grey prediction method to analyze and predict the population loss in Shanxi Province by using the principle of association analysis, and conclude that the population will be lost in the next ten years, but the lost population will gradually decrease.
%K Logistic阻滞增长模型，时间序列，熵权组合，Leslie模型，BP神经网络<br>Logistic Retarded Growth Model
%K Time Series
%K Entropy Weight Combination
%K Leslie Model
%K BP Neural Network
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=50864