Comparative post fatigue residual property predictions of reinforced and unreinforced poly(ethylene terephthalate) fibers using artificial neural networks

2010 ◽  
Vol 41 (3) ◽  
pp. 331-344 ◽  
Author(s):  
Rodney D. Averett ◽  
Mary L. Realff ◽  
Karl I. Jacob
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Ethylene Terephthalate ◽  
Residual Property ◽  
Poly Ethylene Terephthalate ◽  
Artificial Neural ◽  
Poly Ethylene

Modification of PET fabrics by hyperbranched polymer: a comparative study of artificial neural networks (ANN) and statistical approach

2013 ◽  
Vol 33 (5) ◽  
pp. 445-452 ◽  
Author(s):  
Mahdi Hasanzadeh ◽  
Tahereh Moieni ◽  
Bentolhoda Hadavi Moghadam
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Three Dimensional ◽  
Ann Model ◽  
Acid Dyes ◽  
Color Strength ◽  
Ann Models ◽  
Artificial Neural ◽  
Poly Ethylene ◽  
Rsm Model

Abstract Hyperbranched polymers (HBPs) are highly branched, three-dimensional and polydisperse macromolecules and have been employed for modification of poly(ethylene terephthalate) (PET) fabrics. The PET fabrics treatment process parameters, like HBP concentration, temperature and time, play a major role in treatment yield and dyeability of treated PET fabrics by acid dyes. Two different quantitative models, comprising response surface methodology (RSM) and artificial neural networks (ANN), were developed for predicting color strength (K/S value) of treated fabrics. The experiments were conducted based on central composite design (CCD) and a mathematical model was developed. A comparison of the predicted color strength using RSM and ANN was studied. The results obtained indicated that both RSM and ANN models show a very good relationship between the experimental and predicted response values. However, the ANN model shows more accurate results than the RSM model.

FUTURE OF ARTIFICIAL NEURAL NETWORKS IN SPACE EXPLORATION AND SPACE ENGINEERING

2018 ◽  
pp. 6-11
Author(s):  
Kobiljon Kh. Zoidov ◽  
◽  
Svetlana V. Ponomareva ◽  
Daniel I. Serebryansky ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Space Exploration ◽  
Artificial Neural
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