New non-isomorphic detection methods for orthogonal designs

被引:6
|
作者
Ke, Xiao [1 ,2 ]
Fang, Kai-Tai [3 ,4 ]
Elsawah, A. M. [3 ,5 ]
Lin, Yuxuan [3 ]
机构
[1] Shenzhen Technol Univ, Coll Big Data & Internet, Shenzhen 518118, Peoples R China
[2] Hong Kong Baptist Univ, Dept Math, Kowloon Tsai, Hong Kong, Peoples R China
[3] BNU HKBU United Int Coll, Div Sci & Technol, Zhuhai, Peoples R China
[4] Chinese Acad Sci, Key Lab Random Complex Struct & Data Anal, Beijing, Peoples R China
[5] Zagazig Univ, Dept Math, Fac Sci, Zagazig, Egypt
关键词
Hamming distance; Isomorphism; Level permutation; Orthogonal design; Uniformity;
D O I
10.1080/03610918.2020.1844895
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
Two fractional factorial designs are called isomorphic if one can be obtained from the other by relabeling the factors, reordering the runs and switching the levels of factors. Given a set of all orthogonal designs (ODs) with n runs, q levels and s factors, it may have several non-isomorphic subclasses. Once a new OD with this design size is generated, it is interesting to know which subclass it belongs to. In this paper, we review several existing methods, which can classify newly generated ODs to the correct non-isomorphic subclass. We also propose two new non-isomorphic detection methods. They can be utilized for the design classification purpose and take some advantages over the existing methods in terms of computation efficiency and classification capability.
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页码:27 / 42
页数:16
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