Comparison of Statistical Regression, Fuzzy Regression and Artificial Neural Network Modeling Methodologies in Polyester Dyeing

The aim of this study is to investigate, apply and compare statistical regression, fuzzy regression and artificial neural network (ANN) for modeling the color yield in polyester high temperature (HT) dyeing as a function of disperse dyes concentration, temperature and time. The predictive power of the obtained models was evaluated by means of MSE value. The results showed that the model based on statistical regression did not meet the required conditions to be accepted. However, the ANN model with a minimum MSE showed a better predictive capability than the model based on fuzzy regression, although the fuzzy regression model was also acceptable

[1]  Chang-Chiun Huang,et al.  Control of Dye Concentration, pH, and Temperature in Dyeing Processes , 1999 .

[2]  Hongxing Li,et al.  Fuzzy Neural Network Theory and Application , 2004, Series in Machine Perception and Artificial Intelligence.

[3]  Jiansheng Guo,et al.  Predicting the Warp Breakage Rate in Weaving by Neural Network Techniques , 2005 .

[4]  D. M. Nunn,et al.  The Dyeing of Synthetic-polymer and Acetate Fibres , 1979 .

[5]  Sabyasachi Ghoshray,et al.  A linear regression model using triangular fuzzy number coefficients , 1999, Fuzzy Sets Syst..

[6]  G. Hirsch,et al.  Dyeing and chemical technology of textile fibers. Von E. R. Trotman. 6. neubearb. Auflage. ISBN 0‐85264‐267‐9. High Wycombe: Charles Griffen 1984. X, 587 s., geb. £ 27.95. , 1985 .

[7]  Joonwhoan Lee,et al.  A study of the emotional evaluation models of color patterns based on the adaptive fuzzy system and the neural network , 2002 .

[8]  Bernd Möller,et al.  FUZZY-STOCHASTIC EVALUATION OF UNCERTAINTIES IN MATERIAL PARAMETERS OF TEXTILES , 2002 .

[9]  Martin T. Hagan,et al.  Neural network design , 1995 .

[10]  Keith C. C. Chan,et al.  Neural Network Prediction of Human Psychological Perceptions of Fabric Hand , 2004 .

[11]  J. Buckley,et al.  An Introduction to Fuzzy Logic and Fuzzy Sets , 2002 .

[12]  James Gordon Cook.  Handbook of textile fibers. Volume I. Manmade fibres. Volume 2. Natural Fibres , 1984 .

[13]  P. Majumdar,et al.  Predicting the Breaking Elongation of Ring Spun Cotton Yarns Using Mathematical, Statistical, and Artificial Neural Network Models , 2004 .

[14]  H Tavanaei,et al.  Modelling of Colour Yield in Polyethylene Terephthalate Dyeing with Statistical and Fuzzy Regression , 2005 .

[15]  C. J. Kuo,et al.  Using Neural Network Theory to Predict the Properties of Melt Spun Fibers , 2004 .

[16]  Colourists,et al.  The Theory of coloration of textiles , 1989 .

[17]  H. Carter.  Fuzzy Sets and Systems — Theory and Applications , 1982 .

[18]  Ellen C. Carter.  Optical Radiation Measurements. Volume 2: Color Measurement, edited by Franc Grum and C. James Bartleson, Academic, New York, 1980, 400 pp. Price: $39.50 , 1981 .

[19]  Les M. Sztandera,et al.  Soft Computing in Textile Sciences , 2002 .

[20]  R. Viertl,et al.  FUZZY INFORMATION AND STOCHASTICS , 2004 .

[21]  LiMin Fu,et al.  Neural networks in computer intelligence , 1994 .

[22]  Elizabeth A. Peck,et al.  Introduction to Linear Regression Analysis , 2001 .

[23]  Michel Happiette,et al.  AN AUTOMATIC TEXTILE SALES FORECAST USING FUZZY TREATMENT OF EXPLANATORY VARIABLES , 2002 .