Neural network model for efficient localization of a number of mutually arbitrary positioned stochastic EM sources in far-field

2014 ◽  
Author(s):  
Zoran Stankovic ◽  
Nebojsa Doncov ◽  
Ivan Milovanovic ◽  
Bratislav Milovanovic
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Far Field ◽  
Stochastic Em

scholarly journals Efficient DoA Tracking of Variable Number of Moving Stochastic EM Sources in Far-Field Using PNN-MLP Model

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Zoran Stanković ◽  
Nebojša Dončov ◽  
Bratislav Milovanović ◽  
Ivan Milovanović
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Probabilistic Neural Network ◽  
Doa Estimation ◽  
Variable Number ◽  
Angular Distance ◽  
Far Field ◽  
The Neural Network ◽  
Two Stages

An efficient neural network-based approach for tracking of variable number of moving electromagnetic (EM) sources in far-field is proposed in the paper. Electromagnetic sources considered here are of stochastic radiation nature, mutually uncorrelated, and at arbitrary angular distance. The neural network model is based on combination of probabilistic neural network (PNN) and the Multilayer Perceptron (MLP) networks and it performs real-time calculations in two stages, determining at first the number of moving sources present in an observed space sector in specific moments in time and then calculating their angular positions in azimuth plane. Once successfully trained, the neural network model is capable of performing an accurate and efficient direction of arrival (DoA) estimation within the training boundaries which is illustrated on the appropriate example.

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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