Worsted spinning process parameters inversion based on a mixed population genetic-ANN algorithm

DOI

10.2991/iccsae-15.2016.179How to use a DOI?

Keywords

Textile production process; mixed population based - artificial neural network; Parameters inversion; Quality control

Abstract

Demand diversity and individuation, make the textile production process is complicated. To solve the problem of worsted spinning process parameters inversion accuracy, the hybrid population genetic neural network algorithm is presented in this paper(mixed population based - artificial neural network, MPG - ANN), MPG - ANN's advantage lies in three distinct advantages. First, it improve the premature problem of traditional genetic algorithm. Second, predict generalization performance is enhanced and the inversion model. Third, the results of the calculation of stability was improved. Based on the quality index of yarn CV value of worsted spinning the key process parameters for inversion in the process of production, and compared with traditional genetic algorithm is applied to the inversion results, verify the feasibility and effectiveness of MPG - ANN algorithm, the inversion accuracy of 97%, the method not only has an important guiding role in the textile production process quality control, but also has a very good reference for enterprises rapid process development of new product design decision.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jian-Guo Yang
AU  - Jing-wei Xiong
AU  - Lan Xu
PY  - 2016/02
DA  - 2016/02
TI  - Worsted spinning process parameters inversion based on a mixed population genetic-ANN algorithm
BT  - Proceedings of the 2015 5th International Conference on Computer Sciences and Automation Engineering
PB  - Atlantis Press
SP  - 975
EP  - 980
SN  - 2352-538X
UR  - https://doi.org/10.2991/iccsae-15.2016.179
DO  - 10.2991/iccsae-15.2016.179
ID  - Yang2016/02
ER  -