Predicting the Hairiness of Ring Spinning Polyester/Cotton Yarn Using Multiple Regression and Artificial Neural Network Approaches

2010 ◽  
Author(s):  
Bo Zhao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multiple Regression ◽  
Cotton Yarn ◽  
Ring Spinning ◽  
Artificial Neural ◽  
Network Approaches

Prediction of Breaking Elongation of Polyester/Cotton Yarn in Ring Spun Processing

2011 ◽  
Vol 366 ◽  
pp. 108-112
Author(s):  
Bo Zhao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Cotton Yarn ◽  
Ring Spinning ◽  
Breaking Elongation ◽  
Artificial Neural ◽  
Actual Test

The artificial neural network model is used to predict the breaking elongation of polyester/cotton ring spinning yarn in this paper. In order to achieve the objective, a series of trials is conducted. The prediction values and actual test values of which are found to be rather close. Therefore, the artificial neural network model proves to be more feasible in the prediction of breaking elongation of polyester/cotton ring spinning yarn.

Applications of Neural Networks in the Hairiness of Ring Spinning Polyester/Cotton Yarn Prediction

2012 ◽  
Vol 549 ◽  
pp. 1055-1059
Author(s):  
Bo Zhao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Processing Parameters ◽  
Cotton Yarn ◽  
Ring Spinning ◽  
Artificial Neural ◽  
Yarn Hairiness ◽  
Bp Neural Network Model

In this paper, back-propagation (BP) neural network model is introduced and established. This work describes the application of the BP artificial neural network model for the purpose of predicting the polyester/cotton yarn hairiness. This approach has been developed and evaluated with the use of multiple sets of data, comprising of a range of processing parameters. The yarn hairiness of ring spinning is strongly related to the processing parameters. However, it is difficult to establish physical models on the relationship between the processing parameters and the yarn hairiness. Due to the artificial neural network can fully approximate any complex nonlinear system and study dynamic behavior of any serious undetermined system. It has a highly parallel calculation ability, strong robustness and fault tolerance. So using the artificial neural network to predict the polyester/cotton yarn hairiness of ring spinning is a very effective way. The experimental results and corresponding analysis show that the BP neural network model is an efficient technique for the yarn hairiness of ring spinning prediction and has wide prospect in the application of ring spinning yarn production system.

scholarly journals Identification Accuracy of Additional Wave Resistance Through a Comparison of Multiple Regression and Artificial Neural Network Methods

2018 ◽  
Vol 1 (1) ◽  
pp. 197-204
Author(s):  
Tomasz Cepowski
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multiple Regression ◽  
Nonlinear Models ◽  
Linear Equations ◽  
Regression Method ◽  
Wave Resistance ◽  
Identification Accuracy ◽  
Linear Regression Method ◽  
Artificial Neural

Abstract The article presents the use of multiple regression method to identify added wave resistance. Added wave resistance was expressed in the form of a four-state nominal function of: “thrust”, “zero”, “minor” and “major” resistance values. Three regression models were developed for this purpose: a regression model with linear variables, nonlinear variables and a large number of nonlinear variables. The nonlinear models were developed using the author's algorithm based on heuristic techniques. The three models were compared with a model based on an artificial neural network. This study shows that non-linear equations developed through a multiple linear regression method using the author’s algorithm are relatively accurate, and in some respects, are more effective than artificial neural networks.

Air carbon monoxide forecasting using an artificial neural network in comparison with multiple regression

2020 ◽  
Vol 6 (3) ◽  
pp. 1467-1475 ◽  
Author(s):  
Seyedeh Reyhaneh Shams ◽  
Ali Jahani ◽  
Mazaher Moeinaddini ◽  
Nematollah Khorasani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Carbon Monoxide ◽  
Multiple Regression ◽  
Artificial Neural

scholarly journals Comparison of Artificial Neural Network and Multiple Regression for Partial Discharge Sources Recognition

2017 ◽  
Author(s):  
Abdullahi Abubakar Masud ◽  
Firdaus Muhammad-Sukki ◽  
Ricardo Albarracin ◽  
Jorge Alfredo Ardila-Rev ◽  
Siti Hawa Abu-Bakar ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multiple Regression ◽  
Partial Discharge ◽  
Artificial Neural

Estimation of Calorific Values of Some of the Turkish Lignites by Artificial Neural Network and Multiple Regressions

2020 ◽  
Vol 10 (2) ◽  
pp. 154-162
Author(s):  
Engin Özdemir ◽  
Didem Eren Sarici
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multiple Regression ◽  
Regression Models ◽  
Calorific Value ◽  
Volatile Matter ◽  
Ash Content ◽  
Percentage Error ◽  
Multiple Regression Models ◽  
Artificial Neural

Background: The calorific value is the most important and effective factors of lignites in terms of energy resources. Humidity, ash content, volatile matter and sulfur content are the main factors affecting lignite's calorific values. Objective: Determination of calorific value is a process that takes time and cost for businesses. Therefore, estimating the calorific value from the developed models by using other parameters will benefit enterprises in term of time, cost and labor. Method: In this study calorific values were estimated by using artificial neural network and multiple regression models by using lignite data of 30 different regions. As input parameters, humidity, ash content and volatile matter values are used. In addition, the mean absolute percentage error and the significance coefficient values were determined. Results: Mean absolute percentage error values were found to be below 10%. There is a strong relationship between calorific values and other properties (R2> 90). Conclusion: As a result, artificial neural network and multiple regression models proposed in this study was shown to successfully estimate the calorific value of lignites without performing laboratory analyses.

Sign in / Sign up

Export Citation Format

Share Document