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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.

Keywords

ANFIS initial load-extension plain-woven fabric modeling

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

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