scholarly journals Development of artificial neural network models based on experimental data of response surface methodology to establish the nutritional requirements of digestible lysine, methionine, and threonine in broiler chicks

2012 ◽  
Vol 91 (12) ◽  
pp. 3280-3285 ◽  
Author(s):  
M. Mehri
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Network Models ◽  
Nutritional Requirements ◽  
Broiler Chicks ◽  
Neural Network Models ◽  
Artificial Neural Network Models

scholarly journals An analysis of noise pollution emitted by moving MF285 Tractor using different mixtures of biodiesel, bioethanol and diesel through artificial intelligence

2019 ◽  
Vol 38 (2) ◽  
pp. 270-281 ◽  
Author(s):  
Mohammad Ghaderi ◽  
Hossein Javadikia ◽  
Leila Naderloo ◽  
Mostafa Mostafaei ◽  
Hekmat Rabbani
Keyword(s):  
Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Correlation Coefficients ◽  
Network Models ◽  
Noise Pollution ◽  
Neural Network Models ◽  
Diesel Fuels ◽  
Artificial Neural Network Models ◽  
Mean Square Errors

In the present study, the noise pollution from different compositions of biodiesel, bioethanol, and diesel fuels in MF285 Tractor was studied in the second and third gears from two positions: driver and bystander, at 1000 and 1600 r/min, and running on 10 different fuel levels. For data analysis, the ANFIS network, neural network, and response surface methodology were applied. Comparing the means of noise pollution at different levels demonstrated that the B25E6D69 fuel, made up of 25% biodiesel and 6% bioethanol, had the lowest noise pollution. The lowest noise pollution was at 1000 r/min. Although the noise pollution emitted in the third gear was a little more than that emitted in the second gear. All the resultant models, laid by response surface methodology, neural network, and ANFIS had excellent results. Considering the statistical criteria, the best models with high correlation coefficients and low mean square errors were ANFIS, response surface methodology, and artificial neural network models, respectively.

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