Artificial Neural Network For Estimation Nutrient Utilization Based on Chemical Composition on Ruminant Animal Feed

2020 ◽  
Author(s):  
Toto Haryanto ◽  
Anuraga Jayanegara ◽  
Dias Febrisahrozi ◽  
Aziz Kustiyo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Chemical Composition ◽  
Animal Feed ◽  
Nutrient Utilization ◽  
Ruminant Animal ◽  
Artificial Neural

Prediction of Hardenability of Gear Steel Using Stepwise Polynomial Regression and Artificial Neural Network

2010 ◽  
Vol 118-120 ◽  
pp. 332-335
Author(s):  
Xiu Hua Gao ◽  
Tian Yong Deng ◽  
Hao Ran Wang ◽  
Chun Lin Qiu ◽  
Ke Min Qi ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Chemical Composition ◽  
Polynomial Regression ◽  
Gear Steel ◽  
Artificial Neural ◽  
Predicted Values ◽  
Polynomial Regression Model

The prediction of the hardenability of gear steel has been carried using stepwise polynomial regression and artificial neural networks (ANN). The software was programmed to quantitatively predict the hardenability of gear steel by its chemical composition using two calculating models respectively. The prediction results using artificial neural networks have more precise than the stepwise polynomial regression model. The predicted values of the ANN coincide well with the actual data. So an important foundation has been laid for prediction and controlling the production of gear steel.

Austenite Formation Temperature Prediction in Steels Using an Artificial Neural Network

2008 ◽  
Vol 273-276 ◽  
pp. 335-341 ◽  
Author(s):  
M. Arjomandi ◽  
S.H. Sadati ◽  
H. Khorsand ◽  
H. Abdoos
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Chemical Composition ◽  
Empirical Equation ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Austenite Formation ◽  
Data Set ◽  
Artificial Neural ◽  
Experimental Values

Determination of the temperature at which Austenite is formed is one of the important parameters in the heat treatment process. Chemical composition is an effective factor on these temperatures, particularly in steels that are used in various industries. In this research we have made an attempt to determine these temperatures based on the chemical composition of the steel. The technique used for this purpose is feedforward Artificial Neural Network (ANN) with the Back Propagation (BP) learning algorithm. A comparison is made between Ac1, Ac3 temperatures predicted with this model and those from the empirical equation as well as the experimental values obtained from costly and time-consuming tests in scientific and industrial centers for various steels. This comparison indicates that at Ac1, a better agreement exists between the ANN-predicted results and experimental values than the results from the empirical equation and experimental values. At Ac3, the results from the empirical equation are closer to those of the experimental than those predicted from the ANN. This was due to the dispersion of the data set used.

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