Use of Group Method of Data Handling Algorithms for Predicting Heated and Steamed Zone Volumes During Steam Flooding

ICIPEG 2014 ◽  
2015 ◽  
pp. 89-96
Author(s):  
Sami Abdelrahman Musa ◽  
Mariyamni Awang
Keyword(s):  
Group Method ◽  
Data Handling ◽  
Steam Flooding

Application of Group Method of Data Handling with a Shift Operator to Forecasting Financial Indices of Local Budgets

2008 ◽  
Vol 40 (6) ◽  
pp. 17-26
Author(s):  
Valentin N. Tomashevskiy ◽  
Alexander N. Vinogradov ◽  
Yuriy A. Oleinik
Keyword(s):  
Shift Operator ◽  
Group Method ◽  
Data Handling ◽  
Financial Indices

APPLICATION OF ONE WEEK AHEAD WAVE PREDICTION FOR USING GROUP METHOD OF DATA HANDLING

2020 ◽  
Vol 76 (2) ◽  
pp. I_229-I_234
Author(s):  
Keishiro CHIYONOBU ◽  
Sooyoul KIM ◽  
Masahide TAKEDA ◽  
Chisato HARA ◽  
Hajime MASE ◽  
...  
Keyword(s):  
Group Method ◽  
Data Handling ◽  
Wave Prediction

Scoring of Tenders in Construction Projects Using Group Method of Data Handling

2020 ◽  
Vol 24 (7) ◽  
pp. 1996-2008
Author(s):  
Masoud Nouri Mehrabani ◽  
Emadaldin Mohammadi Golafshani ◽  
Mehdi Ravanshadnia
Keyword(s):  
Construction Projects ◽  
Group Method ◽  
Data Handling

Methods of Mathematical Modeling of the Leaching Process of Cobalt-Containing Solutions

2021 ◽  
Vol 316 ◽  
pp. 661-666
Author(s):  
Nataliya V. Mokrova
Keyword(s):  
Mathematical Modeling ◽  
Mathematical Model ◽  
Chemical Kinetics ◽  
Group Method ◽  
Data Handling ◽  
Multistage Processes ◽  
Leaching Process ◽  
Proposed Model ◽  
Simulation Results ◽  
The Mathematical Model

Current cobalt processing practices are described. This article discusses the advantages of the group argument accounting method for mathematical modeling of the leaching process of cobalt solutions. Identification of the mathematical model of the cascade of reactors of cobalt-producing is presented. Group method of data handling is allowing: to eliminate the need to calculate quantities of chemical kinetics; to get the opportunity to take into account the results of mixed experiments; to exclude the influence of random interference on the simulation results. The proposed model confirms the capabilities of the group method of data handling for describing multistage processes.

Predicting carcass energy content and composition in broilers using the group method of data handling-type neural networks

2010 ◽  
Vol 149 (2) ◽  
pp. 249-254 ◽  
Author(s):  
A. FARIDI ◽  
M. MOTTAGHITALAB ◽  
H. DARMANI-KUHI ◽  
J. FRANCE ◽  
H. AHMADI
Keyword(s):  
Neural Networks ◽  
Body Weight ◽  
Goodness Of Fit ◽  
Carcass Composition ◽  
Poultry Meat ◽  
Alternative Methods ◽  
Group Method ◽  
Meat Production ◽  
Data Handling ◽  
Input Variables

SUMMARYThe success of poultry meat production has been strongly related to improvements in growth and carcass yield, mainly by increasing breast proportion and reducing carcass fat. Conventional laboratory techniques for determining carcass composition are expensive, cumbersome and time consuming. These disadvantages have prompted a search for alternative methods. In this respect, the potential benefits from modelling growth are considerable. Neural networks (NNs) are a relatively new option for modelling growth in animal production systems. One self-organizing sub-model of artificial NN is the group method of data handling-type NN (GMDH-type NN). The present study aimed at applying the GMDH-type NNs to data from two studies with broilers in order to predict carcass energy (CEn, MJ/g) content and relative growth (g/g of body weight) of carcass components (carcass protein, breast muscle, leg and thigh muscles, carcass fat, abdominal fat, skin fat and visceral fat). The effective input variables involved in the prediction of CEn and carcass fat content using data from the first study were dietary metabolizable energy (ME, kJ/kg), crude protein (CP, g/kg of diet), fat (g/kg of diet) and crude fibre (CF, g/kg of diet). For data from the second study, the effective input variables involved in the prediction of carcass components were dietary ME (MJ/kg), CP (g/kg of diet), methionine (g/kg of diet), lysine (g/kg of diet) and body weight (kg). Quantitative examination of the goodness of fit, using R2 and error measurement indices, for the predictive models proposed by the GMDH-type NN revealed close agreement between observed and predicted values of CEn and carcass components.

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