A Wavelet View for Unifying Boolean Discrete Functions and Neural Nets through Haar Transform
Summary :
Spectral transform methods have been widely studied for classification and analysis of logic functions. Spectral methods have also been used for logic synthesis, and by use of BDDs, practical-sized synthesis problems have been solved. Wavelet theory has recently attracted the attention of researchers in the signal processing field. The Haar function is used in both spectral methods and in signal processing to obtain spectral coefficients of logic functions of signals. In this paper spectral transform-based analysis of neural nets verifying signal processing and discrete function is presented. A neural net element is defined as a discrete function with multi-valued input signals and multi-valued or binary outputs. The multi-valued variable is realized as a variable (V, W) formed by a pair of a binary value and a multi-value pulse width. The multi-valued encoding is used with the multi-valued Haar function to give meanings to the wavelet coefficients from the view of Boolean algebra. A design example shows that these conceptually different concepts are closely related.
- Publication
- IEICE TRANSACTIONS on Electronics Vol.E80-C No.7 pp.1003-1009
- Publication Date
- 1997/07/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Special Section PAPER (Special Issue on New Concept Device and Novel Architecture LSIs)
- Category
- Neural Networks and Chips
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
Masatoshi SEKINE, "A Wavelet View for Unifying Boolean Discrete Functions and Neural Nets through Haar Transform" in IEICE TRANSACTIONS on Electronics,
vol. E80-C, no. 7, pp. 1003-1009, July 1997, doi: .
Abstract: Spectral transform methods have been widely studied for classification and analysis of logic functions. Spectral methods have also been used for logic synthesis, and by use of BDDs, practical-sized synthesis problems have been solved. Wavelet theory has recently attracted the attention of researchers in the signal processing field. The Haar function is used in both spectral methods and in signal processing to obtain spectral coefficients of logic functions of signals. In this paper spectral transform-based analysis of neural nets verifying signal processing and discrete function is presented. A neural net element is defined as a discrete function with multi-valued input signals and multi-valued or binary outputs. The multi-valued variable is realized as a variable (V, W) formed by a pair of a binary value and a multi-value pulse width. The multi-valued encoding is used with the multi-valued Haar function to give meanings to the wavelet coefficients from the view of Boolean algebra. A design example shows that these conceptually different concepts are closely related.
URL: https://global.ieice.org/en_transactions/electronics/10.1587/e80-c_7_1003/_p
Copy
@ARTICLE{e80-c_7_1003,
author={Masatoshi SEKINE, },
journal={IEICE TRANSACTIONS on Electronics},
title={A Wavelet View for Unifying Boolean Discrete Functions and Neural Nets through Haar Transform},
year={1997},
volume={E80-C},
number={7},
pages={1003-1009},
abstract={Spectral transform methods have been widely studied for classification and analysis of logic functions. Spectral methods have also been used for logic synthesis, and by use of BDDs, practical-sized synthesis problems have been solved. Wavelet theory has recently attracted the attention of researchers in the signal processing field. The Haar function is used in both spectral methods and in signal processing to obtain spectral coefficients of logic functions of signals. In this paper spectral transform-based analysis of neural nets verifying signal processing and discrete function is presented. A neural net element is defined as a discrete function with multi-valued input signals and multi-valued or binary outputs. The multi-valued variable is realized as a variable (V, W) formed by a pair of a binary value and a multi-value pulse width. The multi-valued encoding is used with the multi-valued Haar function to give meanings to the wavelet coefficients from the view of Boolean algebra. A design example shows that these conceptually different concepts are closely related.},
keywords={},
doi={},
ISSN={},
month={July},}
Copy
TY - JOUR
TI - A Wavelet View for Unifying Boolean Discrete Functions and Neural Nets through Haar Transform
T2 - IEICE TRANSACTIONS on Electronics
SP - 1003
EP - 1009
AU - Masatoshi SEKINE
PY - 1997
DO -
JO - IEICE TRANSACTIONS on Electronics
SN -
VL - E80-C
IS - 7
JA - IEICE TRANSACTIONS on Electronics
Y1 - July 1997
AB - Spectral transform methods have been widely studied for classification and analysis of logic functions. Spectral methods have also been used for logic synthesis, and by use of BDDs, practical-sized synthesis problems have been solved. Wavelet theory has recently attracted the attention of researchers in the signal processing field. The Haar function is used in both spectral methods and in signal processing to obtain spectral coefficients of logic functions of signals. In this paper spectral transform-based analysis of neural nets verifying signal processing and discrete function is presented. A neural net element is defined as a discrete function with multi-valued input signals and multi-valued or binary outputs. The multi-valued variable is realized as a variable (V, W) formed by a pair of a binary value and a multi-value pulse width. The multi-valued encoding is used with the multi-valued Haar function to give meanings to the wavelet coefficients from the view of Boolean algebra. A design example shows that these conceptually different concepts are closely related.
ER -
English
