Publication detail

Analysis of Algorithms for Radial Basis Function Neural Network. Springer Verlag:

ŠKORPIL, V. ŠŤASTNÝ, J.

Original Title

Analysis of Algorithms for Radial Basis Function Neural Network. Springer Verlag:

Type

journal article in Web of Science

Language

English

Original Abstract

This paper describes the analysis of algorithms for the hidden layer construction of network and for learning of the Radial Basis Function neural Network (RBFN). We compared results obtained by using of learning algorithms LMS (Least Mean Square) and Gradient Algorithms (GA) and results are obtained by using of algorithms APC-III and K-means for hidden layer contruction of neural network. The principles and algorithms given below have been used in an application for object classification that was developed at Brno University of Technology. This solution is suitable for the research of personal wireless communications and similar systems.

Keywords

Radial basis function, Learning algorithm, Neuron, Hidden layer.

Authors

ŠKORPIL, V.; ŠŤASTNÝ, J.

RIV year

2007

Released

7. 9. 2007

Publisher

Springer

Location

Prague

ISBN

1571-5736

Periodical

Mobile and Wireless Communication Networks

Year of study

2007

Number

1

State

United States of America

Pages from

54

Pages to

62

Pages count

9

BibTex

@article{BUT47411,
  author="Vladislav {Škorpil} and Jiří {Šťastný}",
  title="Analysis of Algorithms for Radial Basis Function Neural Network. Springer Verlag:",
  journal="Mobile and Wireless Communication Networks",
  year="2007",
  volume="2007",
  number="1",
  pages="54--62",
  issn="1571-5736"
}