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

Akira Hirose; Shotaro Yoshida

doi:10.1007/978-3-642-24955-6_63

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

Akira Hirose, Shotaro Yoshida

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 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 language	English
Title of host publication	Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings
Pages	526-531
Number of pages	6
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-642-24955-6_63
Publication status	Published - 2011 Nov 28
Externally published	Yes
Event	18th International Conference on Neural Information Processing, ICONIP 2011 - Shanghai, China Duration: 2011 Nov 13 → 2011 Nov 17

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	7062 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	18th International Conference on Neural Information Processing, ICONIP 2011
Country	China
City	Shanghai
Period	11/11/13 → 11/11/17

Keywords

Complex-valued neural network
function approximation
generalization

ASJC Scopus subject areas

Computer Science(all)
Theoretical Computer Science

Access to Document

10.1007/978-3-642-24955-6_63

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Cite this

Hirose, A., & Yoshida, S. (2011). Comparison of complex- and real-valued feedforward neural networks in their generalization ability. In Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings (PART 1 ed., pp. 526-531). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7062 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-24955-6_63

Comparison of complex- and real-valued feedforward neural networks in their generalization ability. / Hirose, Akira; Yoshida, Shotaro.

Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings. PART 1. ed. 2011. p. 526-531 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7062 LNCS, No. PART 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Hirose, A & Yoshida, S 2011, Comparison of complex- and real-valued feedforward neural networks in their generalization ability. in Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 7062 LNCS, pp. 526-531, 18th International Conference on Neural Information Processing, ICONIP 2011, Shanghai, China, 11/11/13. https://doi.org/10.1007/978-3-642-24955-6_63

Hirose A, Yoshida S. Comparison of complex- and real-valued feedforward neural networks in their generalization ability. In Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings. PART 1 ed. 2011. p. 526-531. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). https://doi.org/10.1007/978-3-642-24955-6_63

Hirose, Akira ; Yoshida, Shotaro. / Comparison of complex- and real-valued feedforward neural networks in their generalization ability. Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings. PART 1. ed. 2011. pp. 526-531 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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