Model Identification in Wavelet Neural Networks Framework

  • A. Zapranis
  • A. Alexandridis
Part of the IFIP International Federation for Information Processing book series (IFIPAICT, volume 296)


The scope of this study is to present a complete statistical framework for model identification of wavelet neural networks (WN). In each step in WN construction we test various methods already proposed in literature. In the first part we compare four different methods for the initialization and construction of the WN. Next various information criteria as well as sampling techniques proposed in previous works were compared to derive an algorithm for selecting the correct topology of a WN. Finally, in variable significance testing the performance of various sensitivity and model-fitness criteria were examined and an algorithm for selecting the significant explanatory variables is presented.


Mean Square Error Hide Unit Wavelet Frame Load Forecast Wavelet Neural Network 
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

© IFIP International Federation for Information Processing 2009

Authors and Affiliations

  • A. Zapranis
    • 1
  • A. Alexandridis
    • 1
  1. 1.Department of Accounting and FinanceUniversity of Macedonia of Economics and Social StudiesThessalonikiGreece

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