Boundedness of Weight Elimination for BP Neural Networks

  • Jian Wang
  • Jacek M. Zurada
  • Yanjiang Wang
  • Jing Wang
  • Guofang Xie
Conference paper

DOI: 10.1007/978-3-319-07173-2_15

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8467)
Cite this paper as:
Wang J., Zurada J.M., Wang Y., Wang J., Xie G. (2014) Boundedness of Weight Elimination for BP Neural Networks. In: Rutkowski L., Korytkowski M., Scherer R., Tadeusiewicz R., Zadeh L.A., Zurada J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2014. Lecture Notes in Computer Science, vol 8467. Springer, Cham

Abstract

Weight elimination can be usefully interpreted as an assumption about the prior distribution of the weights trained in the backpropagation neural networks (BPNN). Weight elimination based on different scaling of weight parameters is of a general form, with the weight decay and subset selection methods as special cases. The applications of this method have been well developed, however, only few references provides more comprehensive theoretical analysis. To address this issue, we investigate the uniform boundedness of the trained weights based on a descriptive proof.

Keywords

backpropagation neural networks weight decay weight elimination boundedness 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jian Wang
    • 1
    • 2
    • 3
  • Jacek M. Zurada
    • 2
    • 4
  • Yanjiang Wang
    • 3
  • Jing Wang
    • 1
  • Guofang Xie
    • 5
  1. 1.Dalian University of TechnologyDalianChina
  2. 2.University of LouisvilleLouisvilleUSA
  3. 3.China University of PetroleumQingdaoChina
  4. 4.Information Technology InstituteUniversity of Social SciencesLodzPoland
  5. 5.Freelance Translator and Amateur MathematicianChina

Personalised recommendations