NN-modelling For Nonlinear Functions Approximations
Author(s)
A.N. Kastania & M.P. Bekakos
Abstract
NN-modelling for nonlinear functions
approximations
A.N. Kastania & M.P. Bekakos
Department of Informatics, Athens University of Economics & Business,
Abstract
In this work the possibility to develop a pattern matching mathematical network
architecture is investigated aiming to the assignment of each pattern to the correct
mathematical modelling class.
These mathematical modelling classes consist of eleven nonlinear models. An
expert neural network for nonlinear functions approximations is built which can
learn different mathematical modelling patterns, thus implying the ability to
recognize different nonlinear mathematical modelling functions. The efficiency
of the method is proved through the experimental results achieved.
1 Introduction
MultiLayer Perceptions (MLPs) can be used to model nonlinear mappings. The
main drawback of the MLPs is their rather slow trainability. The backpropagation
of the error through the layers of the network takes a long time. An alternative
model f
Keywords
Related Book
Other papers in this volume
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