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Soft Computing

, Volume 22, Issue 14, pp 4613–4625 | Cite as

A competitive functional link artificial neural network as a universal approximator

  • Ehsan Lotfi
  • Abbas Ali Rezaee
Methodologies and Application

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.

Keywords

Neural networks MLP FLAN Universal classifier 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Department of Computer EngineeringTorbat-e-Jam Branch, Islamic Azad UniversityTorbat-e-JamIran
  2. 2.Department of Computer Engineering and Information TechnologyPayame Noor UniversityTehranIran

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