基于支持向量机的多传感器信息融合算法  

An Algorithm of Multiple Sensor Information Fusion Based on SVM

作　　者：陶秀凤[1] 周鸣争[1] 

机构地区：[1]安徽工程科技学院电气工程系,安徽芜湖241000

出　　处：《计算机技术与发展》2006年第6期177-179,183,共4页Computer Technology and Development

基　　金：安徽省教育厅自然科学基金资助项目(2004kj033zd)

摘　　要：支持向量机(Support Vector Machine,SVM)是一种基于结构风险最小化原理,具有很高泛化性能的学习算法。针对工业多传感器测控系统中,被测系数与相关参数之间存在有较大的非线性和模糊关系,提出了一种基于支持SVM的多传感器信息融合模型及算法。为小样本、非线性、高维数一类多传感器信息融合问题的建模提供了一种有效的途径。通过对“纸张水份在线测量系统”应用表明,基于SVM的多传感器信息融合模型及算法在测量精度和推广性能上都具有一定的优越性。The support vector machine（SVM） is an algorithm based on structure risk minimizing principle, having high generalization ability. In the course of multiple sensor information fusion of industrial control, sensor has bigger nonlinearity aand fuzzy relation between coefficient and relevant parameter. A kind of model and algorithm of multiple sensor information fusion based on the support vector machine are proposed. The model offered a kind of effective way for little sample, non- linear, high dimension. Through use to＂paper moisture content online measuring system＂, the model and algorithm have certain superiority in measuring precision and performance of popularization.

关 键 词：支持向量机 传感器 信息融合 水份测量 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]