Year: 1994
Type: book
Abstract: Training algorithms for radial basis function (RBF) networks usually consist of an unsupervised procedure for finding the centres and a supervised learning algorithm for udating the connection weights. Good netwiork performance will often be dependant on the RBF centre locations but the K-means clustering or related methods which are often used, can be sensitive to the initial conditions and this can result in local minima and a deterioration in overall network performance. In the present study, a fuzzy clustering scheme is implemented to locate the radial basis function centres in a manner which overcomes the sensitivity to initial conditions and improves overall network performance. Artificial and practical data sets are used to demonstrate the properties of the fuzzy clustering scheme.
Authors: G.L. Zheng, S.A. Billings
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1749607352