IEICE TRANSACTIONS on Fundamentals
A Non-uniform Discrete-Time Cellular Neural Network and Its Stability Analysis
Summary :
In this study, we discuss a discrete-time cellular neural network (DTCNN) and its applications including convergence property and stability. Two theorems about the convergence condition of nonreciprocal non-uniform DTCNNs are described, which cover those of reciprocal one as a special case. Thus, it can be applied to wide classes of image processings, such as associative memories, multiple visual patterns recognition and others. Our DTCNN realized by the software simulation can largely reduce the computational time compared to the continuous-time CNN.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E79-A No.2 pp.252-257
- Publication Date
- 1996/02/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- LETTER
- Category
- Neural Networks
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Chen HE, Akio USHIDE, "A Non-uniform Discrete-Time Cellular Neural Network and Its Stability Analysis" in IEICE TRANSACTIONS on Fundamentals,
vol. E79-A, no. 2, pp. 252-257, February 1996, doi: .
Abstract: In this study, we discuss a discrete-time cellular neural network (DTCNN) and its applications including convergence property and stability. Two theorems about the convergence condition of nonreciprocal non-uniform DTCNNs are described, which cover those of reciprocal one as a special case. Thus, it can be applied to wide classes of image processings, such as associative memories, multiple visual patterns recognition and others. Our DTCNN realized by the software simulation can largely reduce the computational time compared to the continuous-time CNN.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e79-a_2_252/_p
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@ARTICLE{e79-a_2_252,
author={Chen HE, Akio USHIDE, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Non-uniform Discrete-Time Cellular Neural Network and Its Stability Analysis},
year={1996},
volume={E79-A},
number={2},
pages={252-257},
abstract={In this study, we discuss a discrete-time cellular neural network (DTCNN) and its applications including convergence property and stability. Two theorems about the convergence condition of nonreciprocal non-uniform DTCNNs are described, which cover those of reciprocal one as a special case. Thus, it can be applied to wide classes of image processings, such as associative memories, multiple visual patterns recognition and others. Our DTCNN realized by the software simulation can largely reduce the computational time compared to the continuous-time CNN.},
keywords={},
doi={},
ISSN={},
month={February},}
Copy
TY - JOUR
TI - A Non-uniform Discrete-Time Cellular Neural Network and Its Stability Analysis
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 252
EP - 257
AU - Chen HE
AU - Akio USHIDE
PY - 1996
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E79-A
IS - 2
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - February 1996
AB - In this study, we discuss a discrete-time cellular neural network (DTCNN) and its applications including convergence property and stability. Two theorems about the convergence condition of nonreciprocal non-uniform DTCNNs are described, which cover those of reciprocal one as a special case. Thus, it can be applied to wide classes of image processings, such as associative memories, multiple visual patterns recognition and others. Our DTCNN realized by the software simulation can largely reduce the computational time compared to the continuous-time CNN.
ER -
English
