Learning methods for radial basis function networks

Roman Neruda

doi:10.1016/j.future.2004.03.013

Learning methods for radial basis function networks

2005-07-01

69

PRE

AI

DOI：10.1016/j.future.2004.03.013

Original

Original request for help

Save

Abstract

En 中文

RBF networks represent a vital alternative to the widely used multilayer perceptron neural networks. In this paper we present and examine several learning methods for RBF networks and their combinations. A gradient-based learning, the three-step algorithm with unsupervised part, and an evolutionary algorithms are introduced, and their performance compared on benchmark problems from the Proben1 database. The results show that the three-step learning is usually the fastest, while the gradient learning achieves better precision. The best results can be achieved by employing hybrid approaches that combine presented methods. (c) 2004 Elsevier B.V. All rights reserved.

Keywords:

radial basis function networks

hybrid supervised learning

genetic algorithms

benchmarking

Learning methods for radial basis function networks

Abstract

AI Summary

Journal

Organization

Cited Papers