Modelling and prediction of antibacterial activity of knitted fabrics made from silver nanocomposite fibres using soft computing approaches

Antibacterial activity of knitted fabrics has been modelled and predicted by using two soft computing approaches, namely artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS). Four parameters, namely proportion of polyester-silver nanocomposite fibres in yarn, yarn count (diameter), machine gauge and type of fabric (100% polyester or 50:50 polyester-cotton), were used as input parameters for predicting antibacterial activity of knitted fabrics. For each of the input parameters, two fuzzy sets (low and high) were considered to reduce the complexity of ANFIS model. The sixteen linguistic fuzzy rules trained by ANFIS were able to explain the relationship between input parameters and antibacterial activity. A comparison between ANN and ANFIS models has also been presented. Both the models predicted the antibacterial activity of knitted fabrics with very good prediction accuracy in the training and testing data sets with coefficient of determination greater than 0.92 and mean absolute prediction error less than 5%. The robustness of the prediction results against data partitioning between training and testing sets has also been investigated. It is found that prediction accuracy of both the models was quite robust with ANFIS showing better performance with lesser number of training data.