Prediction of Cotton Ring Yarn Evenness Properties from Process Parameters by Using Artificial Neural Network and Multiple Regression Analysis

被引：2

作者：

Zhao, Bo ^{[1
]}

机构：

[1] Zhongyuan Univ Technol, Coll Text, Zhengzhou 450007, Henan, Peoples R China

来源：

ADVANCED RESEARCH ON MATERIAL ENGINEERING, ARCHITECTURAL ENGINEERING AND INFORMATIZATION | 2012年 / 366卷

关键词：

yarn evenness; cotton ring; process parameters; artificial neural network; multiple regression mode; prediction;

D O I：

10.4028/www.scientific.net/AMR.366.103

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

The artificial neural network and multiple regression models have been developed to predict the evenness of cotton ring yam with process parameters such as front roller speed, spindle speed, nip gauge, back draft zone time and roving twist. The efficiencies of prediction of the two models have been experimentally verified, and the predicted evennesses of cotton ring yams from both the models have been compared statistically. An attempt has been made to study the effect of process parameters on yarn evenness. The MSE and mean absolute error of ANN modelare lower titan that of multiple regression model. The results show that the performances of prediction of ANN models are more accurate than those of multiple regression models.

引用

页码：103 / 107

页数：5

共 50 条

[1] Prediction of Thin Place of Polyester/Cotton Ring Yarn Properties from Process Parameters by Using Neural Network and Regression Analysis
Zhao, Bo
[J]. 2012 7TH INTERNATIONAL CONFERENCE ON SYSTEM OF SYSTEMS ENGINEERING (SOSE), 2012, : 18 - 20
[2] PREDICTION OF BLENDED YARN EVENNESS AND TENSILE PROPERTIES BY USING ARTIFICIAL NEURAL NETWORK AND MULTIPLE LINEAR REGRESSION
Malik, Samander Ali
Farooq, Assad
Gereke, Thomas
Cherif, Chokri
[J]. AUTEX RESEARCH JOURNAL, 2016, 16 (02) : 43 - 50
[3] Analysis of cotton ring spun yarn diameter using regression and artificial neural network
Unal, Pelin Gurkan
Ozdil, Nilgun
[J]. INDUSTRIA TEXTILA, 2015, 66 (06): : 317 - 321
[4] Comparison of artificial neural network and linear regression models for prediction of ring spun yarn properties. I. Prediction of yarn tensile properties
Mustafa E. Üreyen
Pelin Gürkan
[J]. Fibers and Polymers, 2008, 9 : 87 - 91
[5] Comparison of artificial neural network and linear regression models for prediction of ring spun yarn properties.: I.: Prediction of yarn tensile properties
Ureyen, Mustafa E.
Gurkan, Pelin
[J]. FIBERS AND POLYMERS, 2008, 9 (01) : 87 - 91
[6] The prediction of cotton ring yarn properties from AFIS fibre properties by using linear regression models
Ureyen, Mustafa E.
Kadoglu, Hueseyin
[J]. FIBRES & TEXTILES IN EASTERN EUROPE, 2007, 15 (04) : 63 - 67
[7] A Prediction Model Based on Linear Regression and Artificial Neural Network Analysis of the Hairiness of Polyester Cotton Winding Yarn
Bo, Zhao
[J]. ADVANCES IN MULTIMEDIA, SOFTWARE ENGINEERING AND COMPUTING, VOL 1, 2011, 128 : 97 - 103
[8] Research on prediction of cotton yarn quality by artificial neural network
Li Hui-jun
Yang Rui
[J]. PROCEEDINGS OF THE FIBER SOCIETY 2009 SPRING CONFERENCE, VOLS I AND II, 2009, : 709 - 712
[9] Comparison of artificial neural network and linear regression models for prediction of ring spun yarn properties.: II.: Prediction of yarn hairiness and unevenness
Ureyen, Mustafa E.
Gurkan, Pelin
[J]. FIBERS AND POLYMERS, 2008, 9 (01) : 92 - 96
[10] Comparison of artificial neural network and linear regression models for prediction of ring spun yarn properties. II. Prediction of yarn hairiness and unevenness
Mustafa E. Üreyen
Pelin Gürkan
[J]. Fibers and Polymers, 2008, 9 : 92 - 96

← 1 2 3 4 5 →