摘要建设用地规模预测是我国土地利用规划的一项重要任务，同时也为土地管理工作提供基本依据，为了切实做好连云港市的土地利用总体规划，并使之更具有科学性、可实施性，必须要做好未来建设用地规模的预测研究工作。39524
本文应用主成分-BP神经网络预测模型对连云港市的建设用地规模进行了预测，首先通过主成分分析法对影响建设用地规模的因素进行了综合分析与处理，提取出影响连云港市建设用地规模的两个主成分，然后采用BP神经网络预测模型预测模型对连云港市2015年-2020年建设用地规模进行预测，得出了未来6年连云港市建设用地规模的预测结果。
研究得出2020年连云港市建设用地面积预测值为162513.4公顷，通过预测值与连云港土地利用总体规划中确定的用地指标进行对比，误差率为：表明：本研究预测结果土地利用总体规划一致性较强。基于主成分-BP神经网络的连云港市建设用地规模预测结果具有可信性，预测结果可以为土地利用优化等研究提供基础数据支持，也为政府土地规划及管理工作提供了相关参考和依据。
毕业论文关键词：主成分--BP神经网络 建设用地 规模预测 连云港市
Abstract Construction land scale prediction is an important task of land use planning in our country, but also provide fundamental basis for land management work, in order to keep the general land use planning of lianyungang, and make it more scientific, practical, must be to do a good job of prediction research scale of construction land in the future.
Principal component - BP neural network prediction model is applied in this article the projections for lianyungang construction land scale, first by principal component analysis on the influencing factors of construction land scale has carried on the comprehensive analysis and processing, to extract the influence of lianyungang construction land scale two principal components, and then USES the BP neural network prediction model prediction model of lianyungang in 2015-2020 to predict the scale of construction land, it is concluded that for the next six years of lianyungang construction land scale prediction results.
Study in 2020, the projections lianyungang construction land area of 162513.4 hectares, determined by prediction and lianyungang in the general land use planning of land use index comparison, error rate is: show that predicted results this study with a strong consistency in general land use planning. Based on principal component - lianyungang construction land scale BP neural network prediction results have credibility, predicted results can provide basic data for the research on optimization of land use and other support, and land planning and management work for the government to provide the related reference and basis.
Keywords: Principal Component--BP Neural Network; Construction Land ;Scale Prediction ;Lianyungang
目录
摘要 I
Abstract II
1 绪 论 1
1.1研究背景和意义 1
1.2国内外研究进展综述 1
1.2.1国内研究进展 1
1.2.2国外研究进展 2
1.3研究的技术路线 3
2 基于主成分分析和BP神经网络的建设用地规模预测模型 3
2.1主成分分析原理 4
2.1.1主成分分析概述 4
2.1.2主成分分析的数学模型 5
2.1.3主成分分析的计算步骤 5
2.2 BP神经网络预测模型原理 5
2.2.1 BP神经网络模型简述 5
2.2.2 BP神经网络设计 6
3 连云港建设用地规模预测 7 基于主成分--BP神经网络模型的连云港建设用地规模预测:http://www.751com.cn/shuxue/lunwen_39850.html