Box and jenkins nonlinear system modelling using RBF neural networks designed by NSGAII
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Date
2015
Journal Title
Journal ISSN
Volume Title
Publisher
Springer
Abstract
In this work, we use radial basis function neural network for modeling
nonlinear systems. Generally, the main problem in artificial neural network is often
to find a better structure. The choice of the architecture of artificial neural network
for a given problem has long been a problem. Developments show that it is often
possible to find architecture of artificial neural network that greatly improves the
results obtained with conventional methods. We propose in this work a method
based on No Sorting Genetic Algorithm II (NSGA II) to determine the best
parameters of a radial basis function neural network. The NSGAII should provide
the best connection weights between the hidden layer and output layer, find the
parameters of the radial function of neurons in the hidden layer and the optimal
number of neurons in the hidden layers and thus ensure learning necessary. Two
functions are optimized by NSGAII: the number of neurons in the hidden layer of
the radial basis function neural network, and the error which is the difference
between desired input and the output of the radial basis function neural network.
This method is applied to modeling Box and Jenkins system. The obtained results
are very satisfactory.
Description
Keywords
NSGAII, Radial basis function (RBF) neural networks, Optimization, Modelling, Non linear system, Box and Jenkins system