文献类型：会议论文

英文题名：The research of building logistics cost forecast based on regression support vector machine

作者：Tian, Jingwen[1]; Gao, Meijuan[1]; Zhou, Shiru[1]

通讯作者：Tian, J.

机构：[1] College of Automation, Beijing Union University, Beijing, China

第一机构：北京联合大学城市轨道交通与物流学院

通讯机构：[1]College of Automation, Beijing Union University, Beijing, China|[1141751]北京联合大学城市轨道交通与物流学院;[11417]北京联合大学;

会议论文集：CIS 2009 - 2009 International Conference on Computational Intelligence and Security

会议日期：December 11, 2009 - December 14, 2009

会议地点：Beijing, China

语种：英文

外文关键词：Artificial intelligence - Buildings - Forecasting - Global optimization - Iterative methods - Support vector machines

摘要：Building logistics cost forecasting is a complicated nonlinear problem, due to the factors that influence building logistics cost are anfratuous, so it was difficult to describe it by traditional methods. The support vector machine (SVM) has the ability of strong nonlinear function approach and strong generalization and it also has the feature of global optimization, in this paper, a modeling and forecasting method of building logistics cost based on SVM is presented. The SVM network structure for forecasting building logistics cost is established. Moreover, we propose a self-adaptive parameter adjust iterative algorithm to confirm SVM parameters, thereby enhancing the convergence rate and the forecasting accuracy. We discussed and analyzed the effect factor of building logistics cost. With the ability of strong self-learning and well generalization of SVM, the modeling and forecasting method can truly forecast the building logistics cost by learning the index information. The actual forecasting results show that this method is feasible and effective. ? 2009 IEEE.