Adaptive linear and normalized combination of radial basis function networks for function approximation and regression
type: Item
instance of: scholarly article
title: Adaptive linear and normalized combination of radial basis function networks for function approximation and regression
zbMATH Open document ID: 1411.68128
DOI: 10.1155/2014/913897
author: Yunfeng Wu
author: Xin Luo
author: Fang Zheng
author: Shanshan Yang
author: Suxian Cai
author: S. C. Ng
published in: Mathematical Problems in Engineering
publication date: 2019-02-08T00:00:00Z
review text: Summary: This paper presents a novel adaptive linear and normalized combination (ALNC) method that can be used to combine the component radial basis function networks (RBFNs) to implement better function approximation and regression tasks. The optimization of the fusion weights is obtained by solving a constrained quadratic programming problem. According to the instantaneous errors generated by the component RBFNs, the ALNC is able to perform the selective ensemble of multiple leaners by adaptively adjusting the fusion weights from one instance to another. The results of the experiments on eight synthetic function approximation and six benchmark regression data sets show that the ALNC method can effectively help the ensemble system achieve a higher accuracy (measured in terms of mean-squared error) and the better fidelity (characterized by normalized correlation coefficient) of approximation, in relation to the popular simple average, weighted average, and the Bagging methods.
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 41A05
zbMATH DE Number: 7017593
Wikidata QID: Q59070696
describes a project that uses: PRMLT
describes a project that uses: UCI-ml
describes a project that uses: bootstrap
describes a project that uses: fda (R)
describes a project that uses: AdaBoost.MH
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/913897
OpenAlex ID: W1999206868
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description: scientific article; zbMATH DE number 7017593