Explicit neural network model for predicting FRP-concrete interfacial bond strength based on a large database

2020 ◽  
Vol 240 ◽  
pp. 111998 ◽  
Author(s):  
Yingwu Zhou ◽  
Songbin Zheng ◽  
Zhenyu Huang ◽  
Lili Sui ◽  
Yang Chen
Keyword(s):  
Neural Network ◽  
Bond Strength ◽  
Network Model ◽  
Neural Network Model ◽  
Large Database ◽  
Interfacial Bond ◽  
Interfacial Bond Strength ◽  
Strength Based

A neural network model of entropy discrimination in pigeons

2003 ◽  
Author(s):  
Christopher Silansky ◽  
Anthony Chemero
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model

A neural network model of pre-pulse inhibition

2003 ◽  
Author(s):  
Nestor Schmajuk ◽  
Roger Smith
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model

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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