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Modeling color change after spinning process using feedforward neural networks
Author(s) -
Thevenet L.,
Dupont D.,
JollyDesodt A. M.
Publication year - 2003
Publication title -
color research and application
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.393
H-Index - 62
eISSN - 1520-6378
pISSN - 0361-2317
DOI - 10.1002/col.10114
Subject(s) - backpropagation , artificial neural network , feed forward , spinning , reflectivity , feedforward neural network , process (computing) , conjugate gradient method , computer science , wavelength , transformation (genetics) , independence (probability theory) , yarn , artificial intelligence , biological system , algorithm , optics , mathematics , engineering , control engineering , physics , chemistry , mechanical engineering , statistics , biochemistry , biology , gene , operating system
This article is concerned with the reflectance spectra prediction based on a neural network developed for yarn from the roving reflectance spectra. The neural network developed is a multilayer feed‐forward network. The first system is wavelength dependent, but its performance is not very satisfactory. The scaled conjugate gradient algorithm is incorporated into the backpropagation procedure to reduce the training phase. Once the wavelength independence of the transformation is established, a second system, whose performances agree with the experimental curves, is proposed. Finally, this system is completed by the introduction of a yarn parameter: the count. The results of this later model are quite promising. © 2002 Wiley Periodicals, Inc. Col Res Appl, 28, 50–58, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.