用WASD神经网络进行德国人口预测的数值和图形结果

Jianzhen Xiao, Siyuan Feng, Yunong Zhang
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引用次数: 1

摘要

人口问题对国家发展、社会稳定和资源配置至关重要。决策者希望弄清出生率、人口规模、人口结构等人口因素,制定有利于国家长期发展的政策。因此,人口预测一直受到许多政府工作人员和学者的高度重视。与其他传统的人口预测方法相比,WASD (weights and structure determination)神经网络不需要大量的人口统计学知识就能达到优异的效果。在本工作中,我们通过数值实验验证了WASD神经网络,表明了WASD神经网络的优异性能。然后对德国人口进行了短期和中期预测,并与其他主流人口预测方法进行了比较。最后,本文提出了德国人口的合理趋势,即近期人口略有下降,但未来10年人口将缓慢增长。
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Numerical and Graphical Results of Germany Population Projection Using WASD Neuronet
Population issues are critical to national development, social stability and resource allocation. Policy-makers hope to Figure out population factors such as birth rate, size, and demographic structure to make policies that are conducive to longterm development of a country. Therefore, population projection has always been highly valued by many government workers and scholars. Compared with other traditional population projection methods, the weights and structure determination (WASD) neuronet does not require a vast knowledge of demography to achieve excellent performance. In this work, we substantiate the WASD neuronet by numerical experiments, which show the excellent performance of the WASD neuronet. Then, we make short-term and mid-term projections of Germany population and also make comparisons with other mainstream population projection methods. Finally, this paper presents a reasonable tendency of Germany population, i.e., declining slightly in near future but growing gently in one decade.
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