首页|辽宁省人口老龄化趋势预测——基于超参数优化的人工神经网络模型

辽宁省人口老龄化趋势预测——基于超参数优化的人工神经网络模型

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为积极应对人口老龄化,依据辽宁省第七次全国人口普查数据,分析了人口老龄化的发展现状和特征.基于人工神经网络模型对辽宁省2022-2035年未来的65周岁以上人口所占比重发展趋势进行了预测分析,结果表明:选用5层神经网络结构模型,每层神经元数量为23,学习率为0.09462,训练得到的相对误差最小为0.0205.预测到2025年、2030年、2035年辽宁省65周岁及以上老年人口所占比重为21.23%、23.01%、23.77%,呈现稳定增长的趋势,老年人口规模不断增加,老龄化程度持续加深.根据模型的预测结果分析,提出了相应的对策建议:即发展老龄产业为老年人口服务;建立和完善以"社区为依托、养老机构为支撑、家庭为核心"的养老服务体系;开发利用老年人力资源,使之老有所用、老有所为.
Prediction of Population Aging Trend in Liaoning Province Based on a Hyperparameter Optimized Artificial Neural Network Model
In order to actively deal with the aging of the population,the development status and characteristics of the aging population are analyzed through the seventh national census data of Liaoning Province.Based on the ar-tificial neural network model,the development trend of the proportion of the population over 65 years old in Lia-oning Province from 2022 to 2035 is predicted.The results show that the structural model of 5 layers of neural network is selected.The number of neurons in each layer is 23;the learning rate is 0.09462,and the minimum rela-tive error obtained from the training is 0.0205.It is predicted that by 2025,2030 and 2035,the proportion of the elderly population aged 65 and above in Liaoning Province will be 21.23%,23.01%and 23.77%,showing a trend of steady growth.The scale of the elderly population is increasing,and the degree of aging continues to deepen.Based on the analysis of the prediction results of the model,the corresponding countermeasures and suggestions are put forward:developing the aging industry to serve the elderly,establishing and improving the pension service system with the community,family and family,developing and utilizing the resources of the elderly.

population aginghyperparameter optimizationartificial neural network modelLiaoning province

邵小妞、刘峰

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大连工业大学艺术与信息工程学院,辽宁大连 116400

大连理工大学建设工程学部,辽宁大连 116023

人口老龄化 超参数优化 人工神经网络模型 辽宁省

2024

绿色科技
花木盆景杂志社

绿色科技

影响因子:0.365
ISSN:1674-9944
年,卷(期):2024.26(7)
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