RADIAL BASIS FUNCTION NEURAL NETWORKS
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Date
2005-05
Authors
WOI, CALEN
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Abstract
A training algorithm for training radial basis function neural networks is presented in this
dissertation. The training algorithm used to train the network is based on the subtractive
clustering method which has a number of advantages such as faster training time and
reduced network architecture compared to the standard radial basis function. The proposed
training algorithm was implemented in the radial basis neural network created by the newrb
function from MA TLAB which already uses gradient based iterative method as the learning
strategy, therefore the new network will undergo a hybrid learning process. The network,
called SC/RBF (Subtractive Clustering - Radial Basis Neural Network) was tested against
the standard radial basis neural network in function approximations and forecasting. Since
the use of neural network techniques to predict air pollutant trend is considered as a reliable
yet cost-effective method for the purpose, the experimental model was tested by forecasting
the pollutant trend at Forth Worth City, Texas with air quality data from Texas Natural
Resources Conservation Commission database.
Description
Keywords
RADIAL BASIS , NEURAL NETWORKS