scholarly journals Wagner, Dändliker und Spenner: Sensors - Vol. 6: Optical Sensors/Barrow: Theorien für Alles/Schuster: Applications of Neural Networks/Press: Numerical Recipes in C: The Art of Scientific Computing/Binder: The Monte Carlo Method in Condensed Matter Physics

1993 ◽  
Vol 49 (7-8) ◽  
pp. 681-684
Author(s):  
E. Manske ◽  
R. W. Gebert ◽  
D. Jäger ◽  
J. Honerkamp ◽  
H. Müller-Krumbhaar ◽  
...  
Keyword(s):  
Neural Networks ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Optical Sensors ◽  
Scientific Computing ◽  
Condensed Matter ◽  
Condensed Matter Physics ◽  
The Monte Carlo Method

The Monte Carlo Method in Condensed Matter Physics

Physics Today ◽  
1993 ◽  
Vol 46 (6) ◽  
pp. 94-95
Author(s):  
K. Binder ◽  
Steven M. Girvin
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Condensed Matter ◽  
Condensed Matter Physics ◽  
The Monte Carlo Method

Estimating the Uncertainty of a Multilayer Perceptron Using the Monte Carlo Method

2012 ◽  
Vol 628 ◽  
pp. 324-329
Author(s):  
F. García Fernández ◽  
L. García Esteban ◽  
P. de Palacios ◽  
A. García-Iruela ◽  
R. Cabedo Gallén
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Multilayer Perceptron ◽  
Good Accuracy ◽  
Output Uncertainty ◽  
The Monte Carlo Method ◽  
Artificial Neural

Artificial neural networks have become a powerful modeling tool. However, although they obtain an output with very good accuracy, they provide no information about the uncertainty of the network or its coverage intervals. This study describes the application of the Monte Carlo method to obtain the output uncertainty and coverage intervals of a particular type of artificial neural network: the multilayer perceptron.

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