Comparison of complex- and real-valued feedforward neural networks in their generalization ability

Akira Hirose, Shotaro Yoshida

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

We compare the generalization characteristics of complex-valued and real-valued feedforward neural networks when they deal with wave-related signals. We assume a task of function approximation. Experiments demonstrate that complex-valued neural networks show smaller generalization error than real-valued ones in particular when the signals have high degree of wave nature.

Original languageEnglish
Title of host publicationNeural Information Processing - 18th International Conference, ICONIP 2011, Proceedings
Pages526-531
Number of pages6
EditionPART 1
DOIs
Publication statusPublished - 2011 Nov 28
Externally publishedYes
Event18th International Conference on Neural Information Processing, ICONIP 2011 - Shanghai, China
Duration: 2011 Nov 132011 Nov 17

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume7062 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other18th International Conference on Neural Information Processing, ICONIP 2011
CountryChina
CityShanghai
Period11/11/1311/11/17

Keywords

  • Complex-valued neural network
  • function approximation
  • generalization

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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