A taxonomy for wavelet neural networks applied to nonlinear modelling

E. Ribes-Gomez, Sean McLoone, George Irwin

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


This article presents a novel classification of wavelet neural networks based on the orthogonality/non-orthogonality of neurons and the type of nonlinearity employed. On the basis of this classification different network types are studied and their characteristics illustrated by means of simple one-dimensional nonlinear examples. For multidimensional problems, which are affected by the curse of dimensionality, the idea of spherical wavelet functions is considered. The behaviour of these networks is also studied for modelling of a low-dimension map.
Original languageEnglish
Pages (from-to)607-627
Number of pages21
JournalInternational Journal of Systems Science
Issue number6
Publication statusPublished - Jun 2008

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computational Theory and Mathematics
  • Management Science and Operations Research


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