Yarn strength modelling and prediction has remained as the cynosure of research for the textile engineers although the investigation in this domain was first reported around one century ago [1,2]. In recent years fuzzy logic has evolved as a very popular prediction technique in textile industry. In the domain of textile technology there are plentiful examples of imprecise variables. For an example, a spinner often uses the terms like ‘fine’ and ‘coarse’ to assess the fibre and yarn count, although these terms do not constitute a well defined boundary. Although fuzzy logic is a powerful tool for dealing with imprecision and uncertainty, however, it has its inherent limitation. This limitation may be minimized by combining it with genetic algorithm (GA) which is a potential tool for global optimization. In this work an effort has been made to improve the prediction performance of fuzzy modelling of cotton yarn strength by developing a hybrid genetic algorithm- fuzzy logic model. This paper deals with modelling of GA- fuzzy model for more accurate prediction of ring spun cotton yarn strength.
Cite this article:
Subhasis Das, Anindya Ghosh. Yarn Strength Prediction using Hybrid Genetic Algorithm - Fuzzy Approach. Int. J. Tech. 4(1): Jan.-June. 2014; Page 109-111
Subhasis Das, Anindya Ghosh. Yarn Strength Prediction using Hybrid Genetic Algorithm - Fuzzy Approach. Int. J. Tech. 4(1): Jan.-June. 2014; Page 109-111 Available on: https://www.ijtonline.com/AbstractView.aspx?PID=2014-4-1-20