Predicting yarn tenacity using soft computing techniques

soft computing approach to model the relationship between the fiber properties, yam parameters and yam tenacity is developed. Because the number of samples is limited, the artificial neural network model to be established must be a small-scale one. Consequently, this soft computing approach includes two stages. Firstly, the structural parameters are selected by utilizing a ranking method, so as to find the most fiber properties as the input variables to fit the small-scale artificial neural network model. The first part of this method takes the human knowledge on the yam tenacity into account. The second part utilizes a data sensitivity criterion based on a distance method. Secondly, the artificial neural network model of the relationship between the fiber properties, yam parameters and yam tenacity is established. The results show that the artificial neural network model yields accurate prediction and a reasonably good artificial neural network model can be achieved with relatively few data points by integrated with the input variable selecting method developed in this research. The results also show that there is great potential for this research in the field of computer assisted design in spinning technology.