支持向量机在航材供应模型中的研究  

The Study of Supporting Vector Machine on Air Equipment Supply Model

作　　者：王斌[1] 刘臣宇[1] 史玉敏[1] 

机构地区：[1]海军航空工程学院青岛分院,青岛266041

出　　处：《价值工程》2010年第29期146-148,共3页Value Engineering

摘　　要：针对部队航材供应量预测过程中,样本采集数目较少的实际情况,采用了一种新的预测方法—支持向量机。该方法基于统计学习理论的原理,较好地解决了小样本的学习问题。并以某部队2000～2007年某项航材供应量为学习样本,建立了该项航材的供应量预测模型。计算结果表明,这种方法比传统的方法具有更少的误差和更好的预测精度。Based on the situation that the number of test samples is few in the course of predicting air equipment annual supply level in training,a new method,support vector machine is given. The algorithm is based on statistical theory. It can better solve learning problem of small sample. By using the historical statistical data of consumption level from 2000 to 2005 as learning sample,a model is built to predict consumption level. The results show that the method can bring less error and better predicted precision compared with the traditional methods.

关 键 词：支持向量机 航材供应量 预测 样本 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TJ41[自动化与计算机技术—控制科学与工程]