Statistical regression and artificial neural network analyses of impinging jet experiments

2008 ◽  
Vol 45 (5) ◽  
pp. 599-611 ◽  
Author(s):  
Nevin Celik ◽  
Irfan Kurtbas ◽  
Nejat Yumusak ◽  
Haydar Eren
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Impinging Jet ◽  
Statistical Regression ◽  
Network Analyses ◽  
Artificial Neural

The Artificial Neural Network and Regression Analysis Approach to Prediction of the Fiber Diameter of Spunbonding Fabrics

2010 ◽  
Vol 33 ◽  
pp. 74-78
Author(s):  
B. Zhao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Network Model ◽  
Process Parameters ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Fiber Diameter ◽  
Statistical Regression ◽  
Artificial Neural

In this work, the artificial neural network model and statistical regression model are established and utilized for predicting the fiber diameter of spunbonding nonwovens from the process parameters. The artificial neural network model has good approximation capability and fast convergence rate, which is used in this research. The results show the artificial neural network model can provide quantitative predictions of fiber diameter and yield more accurate and stable predictions than the statistical regression model, which reveals that the artificial neural network model is based on the inherent principles, and it can yield reasonably good prediction results and provide insight into the relationship between process parameters and fiber diameter.

Entropy analysis and thermal optimization of nanofluid impinging jet using artificial neural network and genetic algorithm

2020 ◽  
Vol 119 ◽  
pp. 104978
Author(s):  
Mohammadamin Mahmoudabadbozchelou ◽  
Amirsaman Eghtesad ◽  
Safa Jamali ◽  
Hossein Afshin
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Impinging Jet ◽  
Entropy Analysis ◽  
Thermal Optimization ◽  
Artificial Neural

scholarly journals Application of Artificial Neural Network(s) in Predicting Formwork Labour Productivity

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Sasan Golnaraghi ◽  
Zahra Zangenehmadar ◽  
Osama Moselhi ◽  
Sabah Alkass
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Labour Productivity ◽  
Quantitative Measure ◽  
General Regression Neural Network ◽  
Statistical Regression ◽  
Inference System ◽  
Holistic View ◽  
Operational Variables ◽  
Artificial Neural

Productivity is described as the quantitative measure between the number of resources used and the output produced, generally referred to man-hours required to produce the final product in comparison to planned man-hours. Productivity is a key element in determining the success and failure of any construction project. Construction as a labour-driven industry is a major contributor to the gross domestic product of an economy and variations in labour productivity have a significant impact on the economy. Attaining a holistic view of labour productivity is not an easy task because productivity is a function of manageable and unmanageable factors. Compound irregularity is a significant issue in modeling construction labour productivity. Artificial Neural Network (ANN) techniques that use supervised learning algorithms have proved to be more useful than statistical regression techniques considering factors like modeling ease and prediction accuracy. In this study, the expected productivity considering environmental and operational variables was modeled. Various ANN techniques were used including General Regression Neural Network (GRNN), Backpropagation Neural Network (BNN), Radial Base Function Neural Network (RBFNN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) to compare their respective results in order to choose the best method for estimating expected productivity. Results show that BNN outperforms other techniques for modeling construction labour productivity.

Using genetic algorithm and artificial neural network analyses to design an Al–Si casting alloy of minimum porosity

2006 ◽  
Vol 27 (7) ◽  
pp. 605-609 ◽  
Author(s):  
S.H. Mousavi Anijdan ◽  
A. Bahrami ◽  
H.R. Madaah Hosseini ◽  
A. Shafyei
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Casting Alloy ◽  
Network Analyses ◽  
Artificial Neural
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