Selected Papers of Wang Yuan 2005
DOI: 10.1142/9789812701190_0035
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A Note on Uniform Distribution and Experimental Design

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Cited by 77 publications
(92 citation statements)
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“…One relatively new design method is called Unifrom Design (UD). Since it was proposed by Fang and Wang in the 1980s (Fang, 1980;Fang et al, 2000;Wang & Fang, 1981), UD has been successfully used in various fields, such as chemistry and chemical engineering, quality and system engineering, computer sciences, survey design, pharmaceuticals, and natural sciences, etc. Generally speaking, uniform design is a space-filling experimental design that allocates experimental points uniformly scattered in the domain.…”
Section: Uniform Experimental Designmentioning
confidence: 99%
“…One relatively new design method is called Unifrom Design (UD). Since it was proposed by Fang and Wang in the 1980s (Fang, 1980;Fang et al, 2000;Wang & Fang, 1981), UD has been successfully used in various fields, such as chemistry and chemical engineering, quality and system engineering, computer sciences, survey design, pharmaceuticals, and natural sciences, etc. Generally speaking, uniform design is a space-filling experimental design that allocates experimental points uniformly scattered in the domain.…”
Section: Uniform Experimental Designmentioning
confidence: 99%
“…The uniform design was created by Fang 34 and Wang and Fang 35 . The goal of uniform design is to find the set of points that most closely approximates a continuous uniform distribution.…”
Section: Design Of Experiments For Computer Simulationsmentioning
confidence: 99%
“…Parameters in the software are settled as populations 40, length of binary encoding 25, crossover probability 0.7 and mutation probability 0.7/(25×2)= 0.014. With evolution of 50 generations, the optimal value and the corresponding solutions are obtained as 1 . In the similar way, with MATLAB GA Toolbox, the problem (12) is also optimized at 50 generations evolution.…”
Section: Global Optimization With Ga For Approaching Modelmentioning
confidence: 99%
“…To this problem, the extensively adopted method is experiment design. It makes use of the regression or variance analysis to determine the optimal parameters after the uniform design or orthogonal experiment [1]. However, there are still several shortcomings: (1) The hardship data analysis after the experiment, (2) The poor adaptability of traditional regression analysis and variance analysis to multi-input/multi-output problems, (3) The difficulty in searching the global optimal parameter combination.…”
Section: Introductionmentioning
confidence: 99%