【摘要】 对区域物流量进行研究与预测有助于把握区域物流的需求,实现区域物流供需相对平衡,提高区域物流规划质量和运行效率具有重要的理论和实际意义.本文将模糊粗糙集理论引入区域物流量的预测中,建立基于模糊粗糙集与支持向量机的区域物流量预测模型,用模糊粗糙集作为前端预处理器对数据进行约简,剔除冗余信息,以实现两种算法的优势互补.针对支持向量机在处理数据时无法将数据简化的问题,提出了基于模糊粗糙集与支持向量机的区域物流量预测方法,在支持向量机对样本数据进行处理之前,利用模糊粗糙集数据挖掘的能力对原始数据样本集进行预处理.结果表明,这种预测方法具有很好的精确性和有效性.更多还原

【Abstract】 The regional logistics research and forecasting is helpful for grasping the regional logistics demand,enforce relative balance between supply and demand of regional logistics and improve the quality of regional logistics planning and operational efficiency.To realize the complementary advantages of two algorithms,this paper introduces the fuzzy-rough set theory into regional logistics forecast.It develops a model for regional logistics forecasting based on the fuzzy rough set and SVM.It then applies the fuzzy rough set as a front-end processor for data reduction and eliminates the redundant information.Because the SVM for processing data in the data reduction cannot be easily simplified,this paper proposes a forecasting method of regional logistics based on the fuzzy rough set and SVM.Before processing the sample data by SVM,the data is used to enlarge the capabilities of fuzzy rough set on original data samples for preprocessing.The results show that this prediction method has good accuracy and effectiveness.更多还原