Predicting configuration performance of modular product family using principal component analysis and support vector machine

作     者：张萌 李国喜 龚京忠 吴宝中 

作者机构：College of Mechatronic Engineering and AutomationNational University of Defense Technology 

出 版 物：《中南大学学报：英文版》 (Journal of Central South University)

年 卷 期：2014年第21卷第7期

页      面：2701-2711页

核心收录：

学科分类：12[管理学] 13[艺术学] 08[工学] 1305[艺术学-设计学（可授艺术学、工学学位）] 0810[工学-信息与通信工程] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0806[工学-冶金工程] 081104[工学-模式识别与智能系统] 0805[工学-材料科学与工程（可授工学、理学学位）] 0703[理学-化学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Project(9140A18010210KG01) supported by the Departmental Pre-Research Fund of China 

主　　题：支持向量机 主成分分析 性能配置 性能预测 模块化 SVM模型 软计算技术 产品性能 

摘      要：A novel configuration performance prediction approach with combination of principal component analysis(PCA) and support vector machine(SVM) was *** method can estimate the performance parameter values of a newly configured product through soft computing technique instead of practical test experiments,which helps to evaluate whether or not the product variant can satisfy the customers individual *** PCA technique was used to reduce and orthogonalize the module parameters that affect the product ***,these extracted features were used as new input variables in SVM model to mine knowledge from the limited existing product *** performance values of a newly configured product can be predicted by means of the trained SVM *** PCA-SVM method can ensure that the performance prediction is executed rapidly and accurately,even under the small sample *** applicability of the proposed method was verified on a family of plate electrostatic precipitators.