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

Akira Hirose, Shotaro Yoshida

研究成果: Conference contribution

7 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルNeural Information Processing - 18th International Conference, ICONIP 2011, Proceedings
ページ526-531
ページ数6
PART 1
DOI
出版ステータスPublished - 2011 11 28
外部発表はい
イベント18th International Conference on Neural Information Processing, ICONIP 2011 - Shanghai, China
継続期間: 2011 11 132011 11 17

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
番号PART 1
7062 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

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

ASJC Scopus subject areas

  • コンピュータ サイエンス(全般)
  • 理論的コンピュータサイエンス

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