(完整版)基于神经网络的中国人口预测算法研究毕业论文

毕业论文（设

计）

题目基于神经网络的中国人口预测

算法研究

所在院(系)数学与计算机科学学院

专业班级信息与计算科学1102班

指导教师赵晖

完成地点陕西理工学院

2015年5 月25日

基于神经网络的中国人口预测算法研究

作者：宋波

（陕理工学院数学与计算机科学学院信息与计算科学专业1102班，陕西汉中

723000）

指导教师：赵晖

[摘要]我国现正处于全面建成小康社会时期,人口发展面临着巨大的挑战,经济社会发展与资源环境的矛盾日益尖锐。我国是个人口大国、资源小国，这对矛盾将长期制约我国经济社会的发展。准确地预测未来人口的发展趋势，制定合理的人口规划和人口布局方案具有重大的理论意义和实用意义。本文介绍了人口预测的概念及发展规律等。

首先，本文考虑到人口预测具有大量冗余、流动范围和数量扩大的特性，又为提高人口预测的效果，因此，使用归一化对人口数据进行了处理，该方法不需要离散化原数据，这样就保证了人口预测的准确性和原始数据的信息完整性。其次，本文提出了一种基于神经网络预测的优化算法，该算法避免了人们在预测中参数选择的主观性而带来的精度的风险，增强了人口预测的准确性。同时，为说明该算法的有效性，又设计了几种人们通常所用的人口模型和灰色预测模型算法，并用相同的数据进行实验，得到了良好的效果，即本文算法的人口预测最为准确，其预测性能明显优于其他算法，而这主要是参数的选择对于增强预测性方面的影响，最终导致人口预测精确度。同时，在算法的稳定性和扩展性方面，该算法也明显优于其他算法。

考虑出生率、死亡率、人口增长率等因素的影响，重建神经网络模型预测人口数量。

[关键词] 神经网络人口模型灰色预测模型软件

Population projections based on neural networks

Author: Song Bo

(Grade11,Class 2, Major in Information and computing science, Mathematics and

computer science Dept.

Tutor:Zhao Hui

Abstract：Our country is now in the period of building a moderately prosperous society, demographic development is faced with great challenges, the contradiction between economic and social development and environmental protection increasingly sharp. Our country is populous country, resources small country, this contradiction will have long hindered the development of economy and society. Accurately predict the future demographic trends, population planning and development of rational population distribution program has great theoretical and practical significance. This paper introduces the concept of population projections and development law and so on.

Firstly, taking into account the population predicted to have a lot of redundancy, to expand the scope and volume of flow characteristics, but also to improve the population projections of the effect, therefore, the use of normalized data were processed on the population, which does not require discrete raw data, this ensures that the population forecast accuracy and completeness of information the original data. Secondly, this paper presents an optimization algorithm based on neural network prediction, the algorithm avoids the people in the forecast parameters and risks subjectivity accuracy, and enhance the accuracy of population projections. Meanwhile, in order to show the effectiveness of the algorithm, and designed several people population model is usually used and the gray prediction model and algorithm, and tested using the same data, obtained good results, that population is the most accurate prediction algorithm, which forecast outperforms other