Artificial Neural Network Prosperities in Textile Applications

Such as other fields, textile industry, deal with numerous large inputs and possible outputs parameters and always feed with a complex interdependence between parameters, it is highly unlikely that an exact mathematical model will ever be developed. Furthermore, since there are many dependent and independent variables during different textile progress, it becomes difficult to conduct and to cover the entire range of the parameters. Moreover, the known and unknown variables cannot be interpolated and extrapolated in a reasonable way based on experimental observations or mill measurements due to the shortage of knowledge on the evaluation of the interaction and significance at weight contributing from each variable. For example, it is quite difficult to develop some universal practical models that can accurately predict yarn quality for different mills (Chattopadhyay & Guha, 2004). Statistical models have also shown up their limitations in use—not least their sensitivity to rogue data—and are rarely used in any branch of the textile industry as a decision-making tool. The mechanistic models proposed by various authors overtly simplify the case to make the equations manageable and pay the price with their limited accuracy. In any case, the vast volume of process parameterrelated data is hardly ever included in these models, making them unsuitable for application in an industrial scenario. By using neural networks, it seems to be possible to identify and classify different textile properties (Guruprasad & Behera, 2010). Some of the studies reported in recent years on the application of neural networks are discussed hereunder.