Use of input selection techniques to improve the performance of an artificial neural network during the prediction of yarn quality properties

2008 ◽  
Vol 108 (1) ◽  
pp. 320-327 ◽  
Author(s):  
Josphat Igadwa Mwasiagi ◽  
Xin Hou Wang ◽  
Xiu Bao Huang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Input Selection ◽  
Quality Properties ◽  
Yarn Quality ◽  
Artificial Neural ◽  
Yarn Quality Properties

Structural optimisation and input selection of an artificial neural network for river level prediction

2008 ◽  
Vol 355 (1-4) ◽  
pp. 192-201 ◽  
Author(s):  
Paul Leahy ◽  
Ger Kiely ◽  
Gearóid Corcoran
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Structural Optimisation ◽  
Input Selection ◽  
River Level ◽  
Artificial Neural ◽  
Selection Of

Ring yarn quality prediction using hybrid artificial neural network

2015 ◽  
Vol 27 (6) ◽  
pp. 940-956 ◽  
Author(s):  
Hanen Ghanmi ◽  
Adel Ghith ◽  
Tarek Benameur
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hybrid Model ◽  
Quality Index ◽  
Content Type ◽  
Yarn Quality ◽  
Spun Yarn ◽  
Yarn Properties ◽  
Ring Spun Yarn ◽  
Artificial Neural

Purpose – The purpose of this paper is to predict a global quality index of a ring spun yarn whose count Ne is ranging between 7.8 (76.92 tex) and 22.2 (27 tex). To fulfill this goal, a hybrid model based on artificial neural network (ANN) and fuzzy logic has been established. Fiber properties, yarn count and twist level are used as inputs to train the hybrid model and the output would be a quality index which includes the major physical properties of ring spun yarn. Design/methodology/approach – The hybrid model has been developed by means of the application of two soft computing approaches. These techniques are ANN which allows the authors to predict four important yarn properties, namely: tenacity, breaking elongation, unevenness and hairiness and fuzzy expert system which investigates spinner experience to give each combination of the four yarn properties an index ranging from 0 to 1. The prediction of the model accuracy was estimated using statistical performance criteria. These criteria are correlation coefficient, root mean square error, mean absolute error and mean relative percent error. Findings – The obtained results show that the constructed hybrid model is able to predict yarn quality from the chosen input variables with a reasonable degree of accuracy. Originality/value – Until now, there is no sufficiently information to evaluate and predict the global yarn quality from raw materials characteristics and process parameters. Therefore, this present paper’s aim is to investigate spinner experience and their understanding about both the impact of various parameters on yarn properties and the relationship between these properties and the global yarn quality to predict a quality index.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural

Forecasting travel demand: a comparison of logit and artificial neural network methods

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travel Demand ◽  
Network Methods ◽  
Artificial Neural

An Artificial Neural Network to Evaluate Cartilage Collagen and Proteoglycan Fractions Based on Multiparametric Quantitative MRI Techniques

2019 ◽  
Author(s):  
Johannes Thüring ◽  
Kevin Linka ◽  
Christiane Kuhl ◽  
Sven Nebelung ◽  
Daniel Truhn
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Quantitative Mri ◽  
Artificial Neural
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