计量学报2009，Vol.30Issue(6)：543-546,4.DOI:10.3969/j.issn.1000-1158.2009.06.14
基于遗传算法和最小二乘支持向量机的织物剪切性能预测
Prediction of Fabric Shearing Property with Least Square Support Vector Machines
摘要
Abstract
A new method is proposed to predict the fabric shearing property with least square support vector machines ( LS-SVM ). The genetic algorithm is investigated to select the parameters of LS-SVM models as a means of improving the LS- SVM prediction. After normalizing the sampling data, the sampling data are inputted into the model to gain the prediction result. The simulation results show the prediction model gives better forecasting accuracy and generalization ability than BP neural network and linear regression method.关键词
计量学/织物/剪切性能/最小二乘支持向量机/遗传算法Key words
Metrology/ Fabric /Shearing property/ Least square support vector mathihe/ Genetic algorithm分类
通用工业技术引用本文复制引用
卢桂馥,王勇,窦易文..基于遗传算法和最小二乘支持向量机的织物剪切性能预测[J].计量学报,2009,30(6):543-546,4.基金项目
安徽省自然科学基金(07041205) （07041205）
安徽省教育厅青年教师科研资助计划(2006jql156) （2006jql156）
安徽工程科技学院青年教师基金 (2005YQ004) （2005YQ004）
