Abstract—The hybrid approach of Neuro-Genetic and Genetic Algorithm techniques is developed to model, to simulate and to predict fibre to yarn spinning process and cost optimization. Starting with cotton, desired yarn is produced on ring frame. The quality and cost of resulting yarn play a significant role to determine its end application. The challenging task of any spinner lies in producing a yarn as percustomer demand with added cost benefit. In this study, a Neuro-genetic concept is used to predict fibre properties for desired yarn. Genetic Algorithm approach is used further for cost optimization. These are combined into the so-called hybrid modeling frame work. The performance of Hybrid innovative model is superior compared to current manual machine intervention. The present model may be a fine framework for development of similar applications for complex model that require prediction and multi-objective optimization.
Index Terms—Artificial Neural Network, Genetic Algorithm, Optimization.
L. S. Admuthe is student with the Shivaji University, Kolhapur, India (phone: +912302421300.
S. D. Apte, was with Walchand Institute of Technology, Sangli, India. She is now with the Department of Electronics, Shahu Institute of Technology, Pune, India.
Cite: L. S. Admuthe and S. D. Apte, "Neuro – Genetic Cost Optimization Model: Application of Textile Spinning Process," International Journal of Computer Theory and Engineering vol. 1, no. 4, pp. 441-444, 2009.
Copyright © 2008-2024. International Association of Computer Science and Information Technology. All rights reserved.
