Function Approximation Using Feedforward Neural Networks

2005 ◽  
pp. 253-294
Keyword(s):  
Neural Networks ◽  
Function Approximation ◽  
Feedforward Neural Networks

A FUNCTION APPROXIMATION ALGORITHM USING SEQUENTIAL COMPOSITION

1993 ◽  
Vol 04 (02) ◽  
pp. 187-199
Author(s):  
RHYS NEWMAN
Keyword(s):  
Neural Networks ◽  
Approximation Algorithm ◽  
Function Approximation ◽  
Feedforward Neural Networks ◽  
New Method ◽  
Convergence Properties ◽  
Noise Stability ◽  
One Dimensional ◽  
Sequential Composition

A new method for approximating one dimensional functions is developed based on structural capabilities of multilayer feedforward neural networks. It possesses notable but unproven convergence properties which are examined in a series of examples. It is shown that it outperforms conventional networks for complicated one dimensional problems. An adaptive version of the algorithm whose approximation changes to best use the data available is also presented. Experiments indicate that this method is very stable in the presence of noise. For straightforward function approximation however, other conventional routines generally perform better. Nevertheless, noise stability, adaptability and other properties make the new method useful in context.

scholarly journals Asymptotic Prediction Error Variance for Feedforward Neural Networks

2020 ◽  
Vol 53 (2) ◽  
pp. 1108-1113
Author(s):  
Magnus Malmström ◽  
Isaac Skog ◽  
Daniel Axehill ◽  
Fredrik Gustafsson
Keyword(s):  
Neural Networks ◽  
Prediction Error ◽  
Feedforward Neural Networks ◽  
Error Variance ◽  
Prediction Error Variance
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