Journal of Fibre & Textile Research
Vol. 31, September 2006, pp. 439-443
F Ameri , S Moradian , M Amani Tehran & K Faez
Received 19 April 2005; revised received 26 July 2005; accepted 29 August 2005
Attempts have been made to use different transformed reflectance functions as input for a fixed genetically optimized neural network match prediction system. Two different sets of data depicting dyed samples of known recipes but metameric to each other were used to train and test the network. All the transformed and untransformed reflectance functions gave good recipe predictions when trained and tested by the same data sets (PF/4 being less than 4). However, the transformation based on matrix R of the decomposition theory showed promising results, since it gave very good colorant concentration predictions when trained by the first set of data dyed with one set of colorants while being tested by a completely different second set of data dyed with a different set of colorants (PF/ 4 always being less than 10).
Keywords: Color match prediction, Matrix R, Neural networks, Transformed reflectance functions, Wool
IPC Code: Int. Cl.8 G06N3/02