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Incremental Approximation by Layer Neural Networks

  • Gabriela Andrejková
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 5)

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

Keywords

Neural Network Solar Wind Hide Layer Convex Hull Interplanetary Magnetic Field 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Gabriela Andrejková
    • 1
  1. 1.Faculty of Science Department of Computer ScienceP. J. Šafárik UniversityKošiceSlovakia

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