RADIAL BASIS FUNCTION NEURAL NETWORKS

dc.contributor.author	WOI, CALEN
dc.date.accessioned	2016-01-12T03:59:55Z
dc.date.available	2016-01-12T03:59:55Z
dc.date.issued	2005-05
dc.description.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.	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/1502
dc.subject	RADIAL BASIS	en_US
dc.subject	NEURAL NETWORKS	en_US
dc.title	RADIAL BASIS FUNCTION NEURAL NETWORKS	en_US
dc.type	Thesis	en_US

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