期刊
INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING
卷 25, 期 4-5, 页码 340-352出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/0951192X.2011.645381
关键词
cost estimation; least squares support vector machines; back-propagation neural networks; statistical regression analysis; data transformation
Industry competitiveness depends on the cost, performance, and timely delivery of the product. Thus, an accurate, rapid, and robust product cost estimation model for the entire product life cycle is essential. This research applies two machine learning methods - back-propagation neural networks (BPNs) and least squares support vector machines (LS-SVMs) - to solve product life cycle cost estimation problems. The performance of a number of cost estimation models, statistical regression analyses, BPNs and LS-SVMs, are compared in terms of their performance. The estimation results and performance reveal that both LS-SVMs and BPNs can both provide more accurate performance than conventional models. We also make the first attempt to combine LS-SVMs and data transformation (DT) techniques to solve the outlier problem in the cost database. The cost estimation of airframe structure manufacturing was used to evaluate the feasibility of this novel combining mechanism. Finally, a more accurate, available, and generalisable cost estimation model is presented. This research can serve as a useful reference for product cost planning and control in industries.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据