Using radial basis function networks for function approximation and classification
type: Item
instance of: scholarly article
title: Using radial basis function networks for function approximation and classification
zbMATH Open document ID: 1264.68137
DOI: 10.5402/2012/324194
author: Yue Wu
author: Hui Wang
author: Biaobiao Zhang
author: Ke-Lin Du
published in: ISRN Applied Mathematics
publication date: 2013-06-11T00:00:00Z
review text: Summary: The radial basis function (RBF) network has its foundation in the conventional approximation theory. It has the capability of universal approximation. The RBF network is a popular alternative to the well-known multilayer perceptron (MLP), since it has a simpler structure and a much faster training process. In this paper, we give a comprehensive survey on the RBF network and its learning. Many aspects associated with the RBF network, such as network structure, universal approimation capability, radial basis functions, RBF network learning, structure optimization, normalized RBF networks, application to dynamic system modeling, and nonlinear complex-valued signal processing, are described. We also compare the features and capability of the two models.
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 41A30
Mathematics Subject Classification ID: 94A12
zbMATH DE Number: 6172940
describes a project that uses: ANFIS
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.5402/2012/324194
OpenAlex ID: W2020488014
Wikidata QID: Q58690836
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description: scientific article; zbMATH DE number 6172940