An artificial neural network approach to prediction of the colorimetric values of the stripped cotton fabrics

Abstract

This paper presents an artificial neural network (ANN) modeling by Levenberg-Marquardt (LM) algorithm for predicting the colorimetric values of the stripped cotton fabrics dyed using commercial reactive dyes. Achieving the expected efficiency in the application of stripping process is a very important aspect for the success of the reproduction. In the study, the predictions of L * and ΔE colorimetric values of stripped cotton samples for different stripping applications by artificial neural network are reported. We set up different network structures with different number of nodes in the hidden layer, the number of inputs and MSE of results as stopping criteria in order to get the best fitting model. According to the result of the best neural network models predicting L * and ΔE, we achieved 97 % of R for both of them. We are able to predict the L * value of the stripped samples using some working parameters as inputs with only 1.2 % error. We think that our results are very promising and the predictions of L * and ΔE values of stripped samples before applying any process are possible using the ANN model set up in the study, especially for L * .

Artificial Neural Network Prosperities in Textile Applications

“…MLP is a feed-forward neural network. In most textile applications a feed-forward network with a single layer of hidden units is used with a sigmoid activation function for the units (Balci et al, 2008). Some studies have decided the number of unites in the hidden layer upon by conducing the trail and error, or genetic algorithm or other optimizing methods and a network with the minimum test-set error is to be used for further analysis.…”

“…are very important qualities which are necessary to be achieved in the dyed goods. If, these properties are different from that of the expected standard, the product has to either been reprocessed or discarded (Balci et al, 2008). Color separation is most important item in pattern printing process so as to secure integrity of printed fabrics product (Kuo et al, 2007).…”

“…In terms of color separation algorithm, their research relied on supervised backwardpropagation neural network to conduct color separation of printed fabrics RGB sub-image, and utilized PANTONE ® standard color ticket to do color matching, so as to realize accurate color separation (Kuo et al, 2007). Balci et al, 2008 presented an artificial neural network (multilayer perceptron) modeling by Levenberg-Marquardt (LM) algorithm for predicting the colorimetric values of the stripped cotton woven fabrics dyed using commercial reactive dyes. They used 90 different network structures with 15 different number of nodes in the hidden layer, 3 level of inputs (10 inputs, 7inputs, and 6 inputs) and 2 level of MSE value of results as stopping criteria in order to get the best fitting model to predict the L* and ∆E colorimetric values of stripped cotton samples.…”

“…Some variations of neural network parameters were tested to determine the best model, and a cross-validation method was used to estimate the generalization error. Also, some modifications of input and output data were made to improve the learning capabilities (Balci et al, 2008). Metamerism is one of the most fundamental perceptual phenomena of the visual system and can be visualized when a part of colored samples in spite of having different spectral reflectance data give the same color coordinates (i. e. match in color) under one specified condition.…”

The prediction of quality characteristics of cotton/elastane core yarn using artificial neural networks and support vector machines

“…used ANN models with the Levenberg-Marquardt algorithm to predict the colorimetric values of striped cotton fabrics. 10 The number of nodes was changed to see the effect of nodes on the prediction. Balci et al.…”