IEICE TRANSACTIONS on Fundamentals
Multiple-Valued Neuro-Algebra
Summary :
A new arithmetic multiple-valued algebra with functional completeness is introduced. The algebra is called Neuro-Algebra for it has very similar formula and architecture to neural networks. Two canonical forms of multiple-valued functions of this Neuro-Algebra are presented. Since the arithmetic operations of the Neuro-Aglebra are basically a weighted-sum and a piecewise linear operations, their implementations are very simple and straightforward. Furthermore, the multiple-valued networks based on the Neuro-Algebra can be trained by the traditional back-propagation learning algorithm directly.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E76-A No.9 pp.1541-1543
- Publication Date
- 1993/09/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- LETTER
- Category
- Neural Networks
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Zheng TANG, Okihiko ISHIZUKA, Hiroki MATSUMOTO, "Multiple-Valued Neuro-Algebra" in IEICE TRANSACTIONS on Fundamentals,
vol. E76-A, no. 9, pp. 1541-1543, September 1993, doi: .
Abstract: A new arithmetic multiple-valued algebra with functional completeness is introduced. The algebra is called Neuro-Algebra for it has very similar formula and architecture to neural networks. Two canonical forms of multiple-valued functions of this Neuro-Algebra are presented. Since the arithmetic operations of the Neuro-Aglebra are basically a weighted-sum and a piecewise linear operations, their implementations are very simple and straightforward. Furthermore, the multiple-valued networks based on the Neuro-Algebra can be trained by the traditional back-propagation learning algorithm directly.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e76-a_9_1541/_p
Copy
@ARTICLE{e76-a_9_1541,
author={Zheng TANG, Okihiko ISHIZUKA, Hiroki MATSUMOTO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Multiple-Valued Neuro-Algebra},
year={1993},
volume={E76-A},
number={9},
pages={1541-1543},
abstract={A new arithmetic multiple-valued algebra with functional completeness is introduced. The algebra is called Neuro-Algebra for it has very similar formula and architecture to neural networks. Two canonical forms of multiple-valued functions of this Neuro-Algebra are presented. Since the arithmetic operations of the Neuro-Aglebra are basically a weighted-sum and a piecewise linear operations, their implementations are very simple and straightforward. Furthermore, the multiple-valued networks based on the Neuro-Algebra can be trained by the traditional back-propagation learning algorithm directly.},
keywords={},
doi={},
ISSN={},
month={September},}
Copy
TY - JOUR
TI - Multiple-Valued Neuro-Algebra
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1541
EP - 1543
AU - Zheng TANG
AU - Okihiko ISHIZUKA
AU - Hiroki MATSUMOTO
PY - 1993
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E76-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 1993
AB - A new arithmetic multiple-valued algebra with functional completeness is introduced. The algebra is called Neuro-Algebra for it has very similar formula and architecture to neural networks. Two canonical forms of multiple-valued functions of this Neuro-Algebra are presented. Since the arithmetic operations of the Neuro-Aglebra are basically a weighted-sum and a piecewise linear operations, their implementations are very simple and straightforward. Furthermore, the multiple-valued networks based on the Neuro-Algebra can be trained by the traditional back-propagation learning algorithm directly.
ER -
English
