scholarly journals Landslide Hazard Zonation in Taleghan watershed by using intelligent systems (Gaussian radial basis function and multilayer perceptron artificial neural networks)

2017 ◽  
Vol 10 (3) ◽  
pp. 3601-3626
Author(s):  
N Salimi ◽  
M FatemiAghda ◽  
M Teshnehlab ◽  
Y Sharafi ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Radial Basis Function ◽  
Basis Function ◽  
Multilayer Perceptron ◽  
Intelligent Systems ◽  
Landslide Hazard ◽  
Hazard Zonation ◽  
Landslide Hazard Zonation ◽  
Artificial Neural

Fast Learning in Neural Networks

2008 ◽  
pp. 91-104
Author(s):  
Darryl Charles ◽  
Colin Fyfe ◽  
Daniel Livingstone ◽  
Stephen McGlinchey
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Continuous Function ◽  
Radial Basis Function ◽  
Supervised Learning ◽  
Basis Function ◽  
Multilayer Perceptron ◽  
Rbf Networks ◽  
Rbf Network ◽  
Fast Learning

We noted in the previous chapters that, while the multilayer perceptron is capable of approximating any continuous function, it can suffer from excessively long training times. In this chapter we will investigate methods of shortening training times for artificial neural networks using supervised learning. (Haykin, 1999) is a particularly good reference for radial basis function, RBF, networks. In this chapter we outline the theory and implementation of a RBF network before demonstrating how such a network may be used to solve one of the previously visited problems, and compare our solutions.

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