基于先验知识的单因素轮换试验轮换顺序优化

doi:10.7523/j.issn.2095-6134.2009.1.002

中国科学院大学学报 ›› 2009, Vol. 26 ›› Issue (1): 9-17.DOI: 10.7523/j.issn.2095-6134.2009.1.002

基于先验知识的单因素轮换试验轮换顺序优化

郭瑞 † 张建方

1中国科学院研究生院数学科学学院，北京100049，2中国科学院研究生院管理学院，北京100080

收稿日期:1900-01-01 修回日期:1900-01-01 发布日期:2009-01-15

The optimization of varying orders in one-factor-at-a-time experimentation basing on prior knowledge

Guo Rui, Zhang Jian-Fang

1School of Mathematical Sciences, Graduate University of Chinese Academy of Sciences, Beijing, 100049;
2School of Management, Graduate University of Chinese Academy of Sciences, Beijing, 100080

Received:1900-01-01 Revised:1900-01-01 Published:2009-01-15

摘要/Abstract

摘要： 单因素轮换试验作为一种简单的试验方法并不被推荐使用，但近来对单因素轮换试验的作用逐渐有了新的认识，认为单因素轮换试验在优化方面有一定优势。基于上述研究，首先对先验知识进行了分类，然后对基于先验知识的单因素轮换试验轮换顺序优选策略进行了研究，得到了一些有意义的结果；并对利用先验知识的单因素轮换试验与相等试验次数的正交设计做了实例比较，得出单因素轮换试验在平均意义下占优的结果，最后给出了总结和展望。

关键词: 单因素轮换, 试验设计, 交互作用, 先验知识, 正交设计

Abstract:

The One-factor-at-a-time (OFAT)plan was not recommended in practice in design of experiment, but following the recent study of OFAT, the effect and role of OFAT are better understood and realized, which has some advantages in optimization . Basing on our latest two papers, we first analyzed the prior knowledge, then studied optimization strategies of varying orders choices in OFAT experimentation based on prior knowledge. Some benefit and useful results for OFAT were gotten by simulation on many examples. Meanwhile, we did the comparison of OFAT based on prior knowledge with the orthogonal array under the equal number of experimental runs; the result is OFAT wins in average. Finally, we gave the conclusions and described the future work.

Key words: one-factor-at-a-time experimentation, design of experiment, interaction, prior knowledge, orthogonal array

郭瑞张建方. 基于先验知识的单因素轮换试验轮换顺序优化[J]. 中国科学院大学学报, 2009, 26(1): 9-17.

Guo Rui, Zhang Jian-Fang. The optimization of varying orders in one-factor-at-a-time experimentation basing on prior knowledge[J]. , 2009, 26(1): 9-17.

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基于先验知识的单因素轮换试验轮换顺序优化

The optimization of varying orders in one-factor-at-a-time experimentation basing on prior knowledge

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