Training two-layer neural network model for tomography data processing

2002 ◽  
Author(s):  
O.T. Kamenev
Keyword(s):  
Neural Network ◽  
Data Processing ◽  
Network Model ◽  
Neural Network Model ◽  
Tomography Data

Self-training neural network model for tomography data processing

1995 ◽  
Author(s):  
Yuri N. Kulchin ◽  
Oleg B. Vitrik ◽  
Oleg T. Kamenev ◽  
Oleg V. Kirichenko ◽  
Yuri S. Petrov ◽  
...  
Keyword(s):  
Neural Network ◽  
Data Processing ◽  
Network Model ◽  
Neural Network Model ◽  
Tomography Data ◽  
Training Neural Network

Concepts of the neural network model for tomography data processing

1995 ◽  
Author(s):  
Yuri N. Kulchin ◽  
Oleg B. Vitrik ◽  
Oleg T. Kamenev ◽  
Oleg V. Kirichenko ◽  
Yuri S. Petrov ◽  
...  
Keyword(s):  
Neural Network ◽  
Data Processing ◽  
Network Model ◽  
Neural Network Model ◽  
The Neural Network ◽  
Tomography Data

Neural Network Model to Calculate the Creep Data Using Size

2017 ◽  
Vol 7 (8) ◽  
pp. 257
Author(s):  
Seetharam .K ◽  
Sharana Basava Gowda ◽  
. Varadaraj
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Software Engineering ◽  
Network Model ◽  
Neural Network Model ◽  
Software Metrics ◽  
Creep Data ◽  
Engineering Software ◽  
Function Points ◽  
Different Levels

In Software engineering software metrics play wide and deeper scope. Many projects fail because of risks in software engineering development[1]t. Among various risk factors creeping is also one factor. The paper discusses approximate volume of creeping requirements that occur after the completion of the nominal requirements phase. This is using software size measured in function points at four different levels. The major risk factors are depending both directly and indirectly associated with software size of development. Hence It is possible to predict risk due to creeping cause using size.

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