A characterization of simple recurrent neural networks with two hidden units as a language recognizer

Azusa Iwata; Yoshihisa Shinozawa; Akito Sakurai

doi:10.1007/978-3-540-69158-7_46

A characterization of simple recurrent neural networks with two hidden units as a language recognizer

Azusa Iwata, Yoshihisa Shinozawa, Akito Sakurai

Department of Administration Engineering

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

Abstract

We give a necessary condition that a simple recurrent neural network with two sigmoidal hidden units to implement a recognizer of the formal language {a ⁿ b ⁿ

Original language	English
Title of host publication	Neural Information Processing - 14th International Conference, ICONIP 2007, Revised Selected Papers
Pages	436-445
Number of pages	10
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-540-69158-7_46
Publication status	Published - 2008 Oct 27
Event	14th International Conference on Neural Information Processing, ICONIP 2007 - Kitakyushu, Japan Duration: 2007 Nov 13 → 2007 Nov 16

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	4984 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	14th International Conference on Neural Information Processing, ICONIP 2007
Country	Japan
City	Kitakyushu
Period	07/11/13 → 07/11/16

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-540-69158-7_46

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Iwata, A., Shinozawa, Y., & Sakurai, A. (2008). A characterization of simple recurrent neural networks with two hidden units as a language recognizer. In Neural Information Processing - 14th International Conference, ICONIP 2007, Revised Selected Papers (PART 1 ed., pp. 436-445). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4984 LNCS, No. PART 1). https://doi.org/10.1007/978-3-540-69158-7_46

A characterization of simple recurrent neural networks with two hidden units as a language recognizer. / Iwata, Azusa; Shinozawa, Yoshihisa; Sakurai, Akito.

Neural Information Processing - 14th International Conference, ICONIP 2007, Revised Selected Papers. PART 1. ed. 2008. p. 436-445 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4984 LNCS, No. PART 1).

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

Iwata, A, Shinozawa, Y & Sakurai, A 2008, A characterization of simple recurrent neural networks with two hidden units as a language recognizer. in Neural Information Processing - 14th International Conference, ICONIP 2007, Revised Selected Papers. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 4984 LNCS, pp. 436-445, 14th International Conference on Neural Information Processing, ICONIP 2007, Kitakyushu, Japan, 07/11/13. https://doi.org/10.1007/978-3-540-69158-7_46

Iwata A, Shinozawa Y, Sakurai A. A characterization of simple recurrent neural networks with two hidden units as a language recognizer. In Neural Information Processing - 14th International Conference, ICONIP 2007, Revised Selected Papers. PART 1 ed. 2008. p. 436-445. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). https://doi.org/10.1007/978-3-540-69158-7_46

Iwata, Azusa ; Shinozawa, Yoshihisa ; Sakurai, Akito. / A characterization of simple recurrent neural networks with two hidden units as a language recognizer. Neural Information Processing - 14th International Conference, ICONIP 2007, Revised Selected Papers. PART 1. ed. 2008. pp. 436-445 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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