Mäkelä E, Juhola K, Juhola M
Department of Mathematics, Statistics and Philosophy, University of Tampere, Finland.
Comput Biol Med. 1999 Jul;29(4):273-81. doi: 10.1016/s0010-4825(99)00009-8.
The development of the Finnish population has been studied in order to predict its probable changes in the 21st century. More generally, the proposed iterative prediction procedure is useful for homogeneous populations in developed countries. The Finnish population is favorable for demographic studies because there exists accurate Finnish population data for a long historical period. Since several factors, for example natality, mortality, average female fertility and standard of living, have an impact on population, its modeling and prediction is an intricate matter. First, neural networks that are often efficient for nonlinear, complex systems were tried. However, it was found that there were far too many input parameters and a critical shortage of data to train and test neural networks. Instead, a straightforward, iterative procedure to predict the future development of the Finnish population was created, in particular giving its probable upper and lower limits.
为了预测芬兰人口在21世纪可能发生的变化,对芬兰人口的发展进行了研究。更一般地说,所提出的迭代预测程序对发达国家的同质人口很有用。芬兰人口有利于进行人口统计学研究,因为有很长一段历史时期的准确芬兰人口数据。由于出生、死亡、平均女性生育率和生活水平等几个因素会对人口产生影响,其建模和预测是一件复杂的事情。首先,尝试了对非线性复杂系统通常有效的神经网络。然而,发现输入参数太多,而用于训练和测试神经网络的数据严重短缺。取而代之的是,创建了一种直接的迭代程序来预测芬兰人口的未来发展,特别是给出其可能的上限和下限。