工业工程2015，Vol.18Issue(2)：28-33,6.DOI:10.3969/j.issn.1007-7375.2015.02.005
基于粗糙集和支持向量机理论的物流需求预测研究
Study on the Logistical Forecasting Method based on Rough Set Theory and Support Vector Machine( SVM)
摘要
Abstract
Proper forecasting models are of strategic significance for upgrading and optimizing logistical in-dustry.Common forecasting methods include increasing rate method, moving average method, time series method, etc.In real applications, many forecasting methods are not so accurate as to ensure validity be-cause logistical data have such features as multi attributes ( including redundant attributes) , non-linear, small sample.A method is proposed that eliminates redundant attributes for reduction based on discern-ibility matrix algorithm with rough set theory improving the SVM model and optimizing the input parameters by genetic algorithm.Detailed steps of the forecasting method are provided and validity examined via cargo data of Guangdong province.关键词
物流需求预测/粗糙集属性约简/支持向量机Key words
logistical demand forecasting/attribute reduction by rough set theory/support vector machine ( SVM)分类
管理科学引用本文复制引用
钟映竑,黄鑫..基于粗糙集和支持向量机理论的物流需求预测研究[J].工业工程,2015,18(2):28-33,6.基金项目
教育部人文社科规划资助项目(12YJA630199) （12YJA630199）
