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Comparison of experimental designs using neural networks
Author(s) -
Lin Yun,
Zhang Zisheng,
Thibault Jules
Publication year - 2009
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.20233
Subject(s) - artificial neural network , design of experiments , factor (programming language) , nonlinear system , mathematics , computer science , algorithm , statistics , artificial intelligence , physics , quantum mechanics , programming language
Experimental designs were compared using stacked‐layer feed‐forward neural networks. Several traditional three‐level designs and uniform designs were investigated using three‐factor linear and nonlinear models. The prediction error was found to be inversely proportional to the number of experiments. Uniform designs displayed better performance than traditional three‐level designs for the same number of experiments. The sum of squares of prediction errors was generally smaller for uniform designs. The performance difference between three‐level designs and uniform designs was attributed to the number of factor levels. This was confirmed by further investigation on random designs with more factor levels.