Pareto genetic design of group method of data handling type neural network for prediction discharge coefficient in rectangular side orifices

2015 ◽  
Vol 41 ◽  
pp. 67-74 ◽  
Author(s):  
Isa Ebtehaj ◽  
Hossein Bonakdari ◽  
Fatemeh Khoshbin ◽  
Hamed Azimi
Keyword(s):  
Neural Network ◽  
Discharge Coefficient ◽  
Group Method ◽  
Data Handling

Elasticity modulus of clayey deposits estimation using group method of data handling type neural network

Measurement ◽  
2018 ◽  
Vol 121 ◽  
pp. 335-343 ◽  
Author(s):  
Seyed Abolhasan Naeini ◽  
Reza Ziaie Moayed ◽  
Afshin Kordnaeij ◽  
Hossein Mola-Abasi
Keyword(s):  
Neural Network ◽  
Elasticity Modulus ◽  
Group Method ◽  
Data Handling

Prediction of rhamnolipid breakthrough curves on activated carbon and Amberlite XAD-2 using Artificial Neural Network and Group Method Data Handling models

2015 ◽  
Vol 206 ◽  
pp. 293-299 ◽  
Author(s):  
Carlos Eduardo de Araújo Padilha ◽  
Carlos Alberto de Araújo Padilha ◽  
Domingos Fabiano de Santana Souza ◽  
Jackson Araújo de Oliveira ◽  
Gorete Ribeiro de Macedo ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Activated Carbon ◽  
Breakthrough Curves ◽  
Group Method ◽  
Data Handling ◽  
Artificial Neural ◽  
Group Method Data Handling

scholarly journals Elements of an algorithm for optimizing a parameter-structural neural network

2016 ◽  
Vol 101 (1) ◽  
pp. 27-35 ◽  
Author(s):  
Maria Mrówczyńska
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Optimization Methods ◽  
Group Method ◽  
Data Handling ◽  
Training Process ◽  
Measurement Results ◽  
Processing Information

Abstract The field of processing information provided by measurement results is one of the most important components of geodetic technologies. The dynamic development of this field improves classic algorithms for numerical calculations in the aspect of analytical solutions that are difficult to achieve. Algorithms based on artificial intelligence in the form of artificial neural networks, including the topology of connections between neurons have become an important instrument connected to the problem of processing and modelling processes. This concept results from the integration of neural networks and parameter optimization methods and makes it possible to avoid the necessity to arbitrarily define the structure of a network. This kind of extension of the training process is exemplified by the algorithm called the Group Method of Data Handling (GMDH), which belongs to the class of evolutionary algorithms. The article presents a GMDH type network, used for modelling deformations of the geometrical axis of a steel chimney during its operation.

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