Abstract
Genetic-Fuzzy (GF) approach is used to model color yield in the high temperature (HT) polyester dyeing as a function of dye concentration, temperature, and time. The proposed method is compared with statistical regression model for their prediction powers using the Mean Square Errors (MSE). The results show that the GF model is preferable to the statistical regression model in at least two ways. First, it enjoys a better predictive power regarding to its minimum MSE. Second, the statistical regression model fails to satisfy the standard requirements.
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References
Goldberg, D.: Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman, Boston (1989)
Sztandera, L.M., Pastore, C.: Soft Computing in Textile Sciences. Springer, Berlin (2003)
Hung, C.C., Yu, W.H.: Control of Dye Concentration, PH, and Temperature in Dyeing Processes. Textile Research Journal 69, 914–918 (1999)
Jahmeerbacusa, M.I., Kistamahb, N., Ramgulamb, R.B.: Fuzzy Control of Dyebath PH in Exhaust Dyeing. Coloration Technology 120, 51–55 (2004)
Marjoniemi, M., Mantysalo, E.: Neuro-Fuzzy Modeling of Spectroscopic Data. Part A- Modelling of Dye Solutions. J.S.D.C. 113, 13–17 (1997)
Marjoniemi, M., Mantysalo, E.: Neuro-Fuzzy Modeling of Spectroscopic Data. Part B– Dye Concentration Prediction. J.S.D.C. 113, 64–67 (1997)
Tavanai, H., Taheri, S.M., Nasiri, M.: Modeling of Colour Yield in Polyethylene Terphethalate Dyeing with Statistical and Fuzzy Regression. Iranian Polymer Journal 14, 954–968 (2005)
Callhof, C., Wulfhort, B.: Neuro-fuzzy Networks- A Tool For the Development of New Yarn Contacting Exemplary on Friction Discs, Man Made Fiber Yarn Book 95 (1999)
Veit, D., Batista-de-Souza, P., Wulfhorst, B.: Application of a Neural Network in the False Twist Texturing Process. Chemical Fibers International 48, 155–156 (1998)
Nasiri, M.: Fuzzy Regression Modeling of False-Twist Texturing Yarn. In: IFSA 2005 Conference, Beijing, China, pp. 142–146 (2005)
Guifen, Y., Jiansheng, G., Yongyuan, Z.: Predicting the Warp Breakage Rate in Weaving by Neural Network Technicqes. Textile Research Journal 75, 274–278 (2005)
Chung-Feng, K.J., Kun-Iuan, H., Yi-Shiuan, W.: Using Neural Network Theory to Predict the Properties of Melt Spun Fibers. Textile Research Journal 74, 840–843 (2004)
Jeng-Jong, L.: A Genetic Algorithm for Searching Weaving Parameters for Woven Fabrics. Textile research journal 73, 105–112 (2003)
Sette, S., Boullart, L., Van Langenhof, L.: Building a Rule Set for the Fiber-to-Yarn Production Process by Means of Soft Computing Techniques. Textile Research Journal 70, 375–386 (2000)
Blaga, M., Draghici, M.: Application of Genetic Algorithms in Knitting Technology. The Journal of the Textile Institute 97, 175–178 (2004)
Thomassey, S., Happiette, M., Castelain, J.M.: An automatic Textile Sales Forecast Using Fuzzy Treatment of Explanatory Variables. European Journal of Operational Research 16, 275–284 (2005)
Peeva, K., Kyosev, Y.: Fuzzy Relational Calculus. Advanced in Fuzzy Systems- Application and Theory, vol. 22 (2005)
Goorden Cook, J.: Handbook of Textile Fibers, man-made fiber, Merro Technical Library (1984)
Johnon, A.: The Theory of Coloration of Textiles, 2nd edn. S.D.C. Pub, Bardford (1989)
Nunn, D.M.: The Dyeing of Synthetic Polymer and Acetate Fibers. Dyers Company Publication, Trust (1979)
Baumann, W., et al.: Determination of Relative Colour Strength and Residual Colour Difference by Means of Reflectance Measurements. J. Soc. Dyers and Colour 103, 100–105 (1987)
Montgomery, D.C., Peek, C.A.: Introduction to Linear Regression Analysis, 2nd edn. J. Wiley, Chichester (1991)
Zimmermann, H.J.: Fuzzy Set Theory and Its Applications, 3rd edn. Kluwer Akademic Publishers, Dordrecht (1996)
Pedrycz, W., Gomidee, F.: An Introduction to Fuzzy Sets, Analysis and Design. Prentice-Hall, Englewood Cliffs (1998)
Muehlenbein, H.: The Breeder Genetic algorithm- a Provable Optimal search Algorithm and Its Application. IEE Colloquium, Application of Genetic Algorithm, London (1994)
Muehlenbein, H., Schlierkamp-Voosen, D.: Predictive Models For the Breeder Genetic Algorithm: I. Continuous Parameter Optimization. Evolutionary Computation 1, 25–49 (1993)
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Nasiri, M., Taheri, S.M., Tarkesh, H. (2007). Applying Genetic-Fuzzy Approach to Model Polyester Dyeing. In: Melin, P., Castillo, O., Ramírez, E.G., Kacprzyk, J., Pedrycz, W. (eds) Analysis and Design of Intelligent Systems using Soft Computing Techniques. Advances in Soft Computing, vol 41. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72432-2_62
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DOI: https://doi.org/10.1007/978-3-540-72432-2_62
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