Probabilistic Neural Network Model for the In Silico Evaluation of Anti-HIV Activity and Mechanism of Action

2006 ◽  
Vol 49 (3) ◽  
pp. 1118-1124 ◽  
Author(s):  
Santiago Vilar ◽  
Lourdes Santana ◽  
Eugenio Uriarte
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Mechanism Of Action ◽  
Neural Network Model ◽  
In Silico ◽  
Probabilistic Neural Network ◽  
Anti Hiv

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.

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