Skip to main content
SpringerLink
Log in

Incremental Approximation by Layer Neural Networks

  • Conference paper
The State of the Art in Computational Intelligence

Part of the book series: Advances in Soft Computing ((AINSC,volume 5))

  • 222 Accesses

Abstract

We study incremental algorithms operating on one- and two-hidden-layer neural networks with linear output units in such way that in each iteration, some new hidden units are put in the first or in the second hidden layer. The weight parameters of the new units are determined and output weights of all units are recalculated. We apply the algorithms to the special class of functions (for predictions of geomagnetic storms).1

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Andrejkovâ, G., Azorovâ, J., Kudela, K.: Artificial neural networks in prediction of D5t Index. Proceedings of the 1-th Slovak Neural Networks Symposium, Vol. II, Herlany, 1996, 51–59

    Google Scholar 

  2. Barron, A. R. (1993) Universal approximation bounds for superposition of sigmoidal function. IEEE Transactions on Information Theory 39, 930–945

    Article  MathSciNet  MATH  Google Scholar 

  3. Beliczynski, B. (1996): An almost analytical design of incremental discrete functions approximation by one-hidden-layer neural networks. In Proceedings of WCNN’96, 988–991, Lawrence Erlbaum, San Diego.

    Google Scholar 

  4. Jones, L. K. (1992) A simple lemma on greedy approximation in Hilbert space and convergence rates for projection pursuit regression and neural network training. Annual of Statistics, 20, 608–613

    Article  MATH  Google Scholar 

  5. Knrkovâ, V. (1995) Approximation of Functions by Perceptron Networks with Bounded Number of Hidden Units, Neural Networks, Vol. 8, No. 5, 745–750

    Article  Google Scholar 

  6. Knrkovâ, V., Beliczynski, B. (1995) Incremental approximation by one-hiddenlayer neural networks. In proceedings of ICANN’95, pp. I.505–510, Paris: EC2und Cie.

    Google Scholar 

  7. Knrkovâ, V. (1998) Incremental approximation by neural networks. In Complexity: Neural Network Approach. Eds. K. Warwick, M. Kârny, V. Knrkovâ, Springer, 177–188.

    Google Scholar 

  8. Knrkovâ, V. (2000) Incremental Approximation by Neural Networks, Proceedings of NC’2000, Berlin, p. 12.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Science Department of Computer Science, P. J. Šafárik University, Jesenná 5, 041 54, Košice, Slovakia

    Gabriela Andrejková

Authors
  1. Gabriela Andrejková
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Faculty of Electrical Engineering and Informatics, Technical University of Košice, Letna 9/B, 040 00, Košice, Slovakia

    Peter Sinčák  & Ján Vaščák  & 

  2. Faculty of Chemical Technology, Slovak University of Technology Bratislava, Radlinskeho 9, 812 37, Bratislava, Slovakia

    Vladimír Kvasnička

  3. Faculty of Civil Engineering, Slovak University of Technology Bratislava, Radlinskeho 11, 813 68, Bratislava, Slovakia

    Radko Mesiar

Rights and permissions

Reprints and permissions

Copyright information

© 2000 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Andrejková, G. (2000). Incremental Approximation by Layer Neural Networks. In: Sinčák, P., Vaščák, J., Kvasnička, V., Mesiar, R. (eds) The State of the Art in Computational Intelligence. Advances in Soft Computing, vol 5. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1844-4_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-7908-1844-4_3

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-1322-7

  • Online ISBN: 978-3-7908-1844-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation