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Application of an Adaptive Neuro-Fuzzy System for Prediction of Initial Load-Extension Behavior of Plain-woven Fabrics

Department of Textile Engineering, Amirkabir University of Technology, Tehran, Iran

^* To whom correspondence should be addressed. E-mail: amani{at}aut.ac.ir.

	Abstract

An adaptive neuro-fuzzy inference system (ANFIS) allows a level of flexibility over traditional mathematical models in defining and evaluating constraints. This flexibility is very important in modeling complicated non-linear processes, where the system behavioral characteristics cannot be defined precisely. This paper introduces an ANFIS for predicting initial load–extension behavior of plain-woven fabrics. Input values defined as combination expressions of geometrical parameters of fabric and yarn flexural rigidity, which were extracted from Leaf's mathematical model. The results show that the neuro-fuzzy system can be used for modeling initial modulus in the warp and weft directions of plain-woven fabrics. Outputs of the neuro-fuzzy model were also compared with results obtained by Leaf's models.

First published on October 13, 2009
Textile Research Journal 2009, doi:10.1177/0040517509346451


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