Abstract
The objective of this paper is to investigate the predictability of some of woven fabric properties using artificial neural network (ANN) and regression models. For achieving this purpose, a neural network with three layers was adopted. The regression model was of type a multiple – linear regression one. The independent variables were weft yarn count, twist multiplier and weft density; and the dependent ones were tensile strength, breaking extension and air permeability of the woven fabrics. The ANN and regression models were assessed using the Root means square error (RMSE) and the coefficient of determination (R2-value). The findings of this study revealed that ANN is superior to regression model in predicting the woven fabric properties.
Highlights
Artificial neural networkArtificial Neural Networks (ANNs) are algorithmic structures derived from a simplified concept of the human brain structure
Dependent variables which predicted by artificial neural network (ANN) and regression models were tensile strength, breaking extension and air permeability
The findings of this study revealed that R2 values associated with ANN models for all fabric properties were very high compared to the R2 values accompanied the regression models
Summary
Artificial Neural Networks (ANNs) are algorithmic structures derived from a simplified concept of the human brain structure. They belong to the Soft Computing family of methods, along with fuzzy logic/ fuzzy control algorithms and genetic algorithms [1]. They all share an iterative, non-linear search for optimal or suboptimal solutions to a given problem, without the presupposition of a model of any type for the underlying system or process [2]. The weights represent information being used by the neural network model to solve a problem. One of the central issues in neural network design is to utilize systematic procedures (a training algorithm) to modify the weights directly from the training data without any assumptions about the data’s statistical distribution [5]
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