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Soft Computing
Published in: Soft Computing 14/2018

29-05-2017 | Methodologies and Application

A competitive functional link artificial neural network as a universal approximator

Authors: Ehsan Lotfi, Abbas Ali Rezaee

Published in: Soft Computing | Issue 14/2018

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Abstract

In this article, a competitive functional link artificial neural network (C-FLANN) is proposed for function approximation and classification problems. In contrast to the traditional functional link artificial neural networks (FLANNs), the novel structure is a universal approximator and can be used for various applications. C-FLANN is a single-layered feed-forward neural network that enjoys from the concepts of expanded inputs, information capacity units (ICUs) and a winner-take-all competition among the ICUs. These features increase the information capacity of the model without adding the hidden neurons. In the experimental studies, the proposed method is tested on function approximation problems as well as classification applications. Various comparisons with related algorithms such as improved swarm optimization-based FLANN, random vector FLANN and a multilayer perceptron indicate the superiority of the approach in terms of higher accuracy.
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Appendix
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Metadata
Title
A competitive functional link artificial neural network as a universal approximator
Authors
Ehsan Lotfi
Abbas Ali Rezaee
Publication date
29-05-2017
Publisher
Springer Berlin Heidelberg
Published in
Soft Computing / Issue 14/2018
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-017-2644-1

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