文献类型：期刊文献

英文题名：Genetic algorithm-least squares support vector regression based predicting and optimizing model on carbon fiber composite integrated conductivity

作者：Yang, Z.[1];Gu, X. S.[2];Liang, X. Y.[1];Ling, L. C.[1]

机构：[1]E China Univ Sci & Technol, Minist Educ, State Key Lab Chem Engn, Key Lab Specially Funct Polymer Mat & Related Tec, Shanghai 200237, Peoples R China;[2]E China Univ Sci & Technol, Inst Sci Informat, Shanghai 200237, Peoples R China

年份：2010

卷号：31

期号：3

起止页码：1042

外文期刊名：MATERIALS & DESIGN

收录：;EI(收录号：20095212568378);WOS:【SCI-EXPANDED(收录号:WOS:000274203200002)】；

基金：This work was funded by Nature Science Foundation of China under Grant No. of 50672025 and by Science and Technology Commission of Shanghai Municipality under Grant Nos. of 065258033 and 06DZ22003.

语种：英文

外文关键词：Carbon fiber composite; Integrated conductivity; Least squares support vector regression; Genetic algorithms; Modeling

摘要：Support vector machine (SVM), which is a new technology solving classification and regression, has been widely used in many fields. In this study, based on the integrated conductivity(including conductivity and tensile strength) data obtained by carbon fiber/ABS resin matrix composites experiment, a predicting and optimizing model using genetic algorithm-least squares support vector regression (GA-LSSVR) was developed. In this model, genetic algorithm (GA) was used to select and optimize parameters. The predicting results agreed with the experimental data well. By comparing with principal component analysis-genetic back propagation neural network (PCA-CABPNN) predicting model, it is found that GA-LSSVR model has demonstrated superior prediction and generalization performance in view of small sample size problem. Finally, an optimized district of performance parameters was obtained and verified by experiments. It concludes that GA-LSSVR modeling method provides a new promising theoretical method for material design. (C) 2009 Elsevier Ltd. All rights reserved.