Possibilistic regression in false-twist texturing

Authors:
S. M. Taheri;H. Tavanai;M. Nasiri
Affiliations:
School of Mathematical Sciences, Isfahan University of Technology, Isfahan, Iran;Department of Textile Engineering, Isfahan University of Technology, Isfahan, Iran;Department of Textile Engineering, Isfahan University of Technology, Isfahan, Iran
Venue:
ISTASC'06 Proceedings of the 6th WSEAS International Conference on Systems Theory & Scientific Computation
Year:
2006

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Abstract

A possibilistic linear regression, i.e. a linear regression with possibilistic coefficients, is explained. The application of such possibilistic regression method for modeling of twist liveliness of false twist textured nylon yarns as a function of percentage retraction has been studied, based on a few available data. It turns out that possibilistic regression method is superior to conventional statistical regression, when a very small number of observations are available. In such cases the basic assumptions, under which statistical regression analysis is valid, can not be investigated. Based on some criterions, such as the total vagueness of models and the mean of predictive capabilities, the optimum fuzzy model has been derived.