Color recipe prediction with neural networks

The textile is colored most of the time for giving a more attractive appearance or effect. In these colorings, in order to have exactly the same color; color recipe is used for the textile that wanted to be the same color but dyed in different times and with different dyeing machines. Because of this, color recipe prediction has a very important place in the textile industry. Computerized color measurement devices used in dye houses are important devices for color recipe prediction. Predicting the color recipe correctly increases the dyeing performance; decreases complete dyeing process time and decrease the possible errors that are likely to be made.There are a lot of methods used in color recipe prediction. However, because of the nonlinear structure of the color recipes, in this thesis color recipe prediction has been performed by using artificial neural networks and fuzzy logic. While making these predictions, the used data groups made according to CIE system (Lab, Lch, XYZ) and reflectance values. With various programs; radial basis function neural network (RBF NN), feed-forward multilayer perceptron neural network (MLP NN) and fuzzy logic developed in MATLAB were used for calculating the color recipe and their results were compared in detail.In the applications with these three methods it was seen that as the data quantity for training was increased from 250 to 400, the error percentage of the system decreased to 0%. This shows that the quantity of training data is very important for successful training of the system.As a result in all three methods the success of 100% was achieved, however RBF was the most successful method compared to MLP and fuzzy logic. RBF was both faster and was also more stable than the others. It reaches to 0% error value faster.