2231-3915 (Online)
2231-3907 (Print)

Author(s): Subhasis Das, Anindya Ghosh

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Address: Subhasis Das, Anindya Ghosh
Government College of Engineering and Textile Technology, Berhampore, India-742 101
*Corresponding Author

Published In:   Volume - 4,      Issue - 1,     Year - 2014

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

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RNI: Not Available                     
DOI: 10.5958/2231-3915 

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