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This paper proposes the binary second-order recurrent neural networks (BSRNN) equivalent to the modified finite automata (MFA) and presents the learning algorithm to construct the stable BSRNN for inferring regular grammar. This network combines two trends; one is to transform strings of a regular grammar into a recurrent neural network through training with no restriction of the number of neurons, the number of strings, and the length of string and the other is to directly transform itself into a finite automaton. Since neurons in the BSRNN employ a hard-limiter activation functions, the proposed BSRNN can become a good alternative of hardware implementation for regular grammars and finite automata as well as grammatical inference.

- Publication
- IEICE TRANSACTIONS on Information Vol.E83-D No.11 pp.1996-2007

- Publication Date
- 2000/11/25

- Publicized

- Online ISSN

- DOI

- Type of Manuscript
- PAPER

- Category
- Biocybernetics, Neurocomputing

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Soon-Ho JUNG, Hyunsoo YOON, "Binary Second-Order Recurrent Neural Networks for Inferring Regular Grammars" in IEICE TRANSACTIONS on Information,
vol. E83-D, no. 11, pp. 1996-2007, November 2000, doi: .

Abstract: This paper proposes the binary second-order recurrent neural networks (BSRNN) equivalent to the modified finite automata (MFA) and presents the learning algorithm to construct the stable BSRNN for inferring regular grammar. This network combines two trends; one is to transform strings of a regular grammar into a recurrent neural network through training with no restriction of the number of neurons, the number of strings, and the length of string and the other is to directly transform itself into a finite automaton. Since neurons in the BSRNN employ a hard-limiter activation functions, the proposed BSRNN can become a good alternative of hardware implementation for regular grammars and finite automata as well as grammatical inference.

URL: https://global.ieice.org/en_transactions/information/10.1587/e83-d_11_1996/_p

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@ARTICLE{e83-d_11_1996,

author={Soon-Ho JUNG, Hyunsoo YOON, },

journal={IEICE TRANSACTIONS on Information},

title={Binary Second-Order Recurrent Neural Networks for Inferring Regular Grammars},

year={2000},

volume={E83-D},

number={11},

pages={1996-2007},

abstract={This paper proposes the binary second-order recurrent neural networks (BSRNN) equivalent to the modified finite automata (MFA) and presents the learning algorithm to construct the stable BSRNN for inferring regular grammar. This network combines two trends; one is to transform strings of a regular grammar into a recurrent neural network through training with no restriction of the number of neurons, the number of strings, and the length of string and the other is to directly transform itself into a finite automaton. Since neurons in the BSRNN employ a hard-limiter activation functions, the proposed BSRNN can become a good alternative of hardware implementation for regular grammars and finite automata as well as grammatical inference.},

keywords={},

doi={},

ISSN={},

month={November},}

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TY - JOUR

TI - Binary Second-Order Recurrent Neural Networks for Inferring Regular Grammars

T2 - IEICE TRANSACTIONS on Information

SP - 1996

EP - 2007

AU - Soon-Ho JUNG

AU - Hyunsoo YOON

PY - 2000

DO -

JO - IEICE TRANSACTIONS on Information

SN -

VL - E83-D

IS - 11

JA - IEICE TRANSACTIONS on Information

Y1 - November 2000

AB - This paper proposes the binary second-order recurrent neural networks (BSRNN) equivalent to the modified finite automata (MFA) and presents the learning algorithm to construct the stable BSRNN for inferring regular grammar. This network combines two trends; one is to transform strings of a regular grammar into a recurrent neural network through training with no restriction of the number of neurons, the number of strings, and the length of string and the other is to directly transform itself into a finite automaton. Since neurons in the BSRNN employ a hard-limiter activation functions, the proposed BSRNN can become a good alternative of hardware implementation for regular grammars and finite automata as well as grammatical inference.

ER -