广西民族大学学报：自然科学版2012，Vol.18Issue(4)：30-34,5.
主成分分析在线性模型与非线性模型的应用研究
Linear Model and Nonlinear Model based on Principal Components Analysis and Its Application
摘要
Abstract
In order to evaluate the potential forecast efficiency of principal components analysis (PCA) in linear model and nonlinear model, based on 2001-2011 historical tropical cyclone observation data, the PCA efficiency are investigated through multiple linear regression model and neural network model focusing on the northwestern Pacific Ocean tropical cyclone intensity prediction technology. According to these main factors, the input samples of linear regress model and BP neutral network are definite, and the models could be trained to predict tropical cyclone intensity. Result shows that PCA reduces the models dimension of line- ar regression and BP neural network, and weaken multi-collinearity among the independent variables, and the method based on PCA lessens average absolute error (MAE) of tropical cyclone intensity.关键词
主成分分析/线性模型/非线性模型/台风强度Key words
principal components analysis/linear model/nonlinear model/tropical cyclone intensity分类
数理科学引用本文复制引用
农吉夫..主成分分析在线性模型与非线性模型的应用研究[J].广西民族大学学报：自然科学版,2012,18(4):30-34,5.基金项目
国家自然科学基金 （）
广西教育厅科研项目（201204LX083）. （）
