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

Author

Nasiri, Maryam ; Shanbeh, Mohsen ; Tavanai, Hossein

Author_Institution

Dept. of Textile Eng., Isfahan Univ. of Technol.

Volume

1

fYear

2005

fDate

28-30 Nov. 2005

Firstpage

505

Lastpage

510

Abstract

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

Keywords

dyeing; fuzzy set theory; mean square error methods; neural nets; polymer fibres; regression analysis; textile fibres; textile industry; artificial neural network model; color yield model; fuzzy regression model; mean square error method; polyester dyeing; predictive power; statistical regression model; Artificial neural networks; Fuzzy neural networks; Fuzzy set theory; Intelligent networks; Predictive models; Random variables; Reactive power; Statistical analysis; Temperature distribution; Textile technology;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence for Modelling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, International Conference on

Conference_Location

Vienna

Print_ISBN

0-7695-2504-0

Type

conf

DOI

10.1109/CIMCA.2005.1631314

Filename

1631314