The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Rangkaian Neural Selular (CNN) telah dibangunkan sebagai platform pemprosesan isyarat selari berkelajuan tinggi. Dalam makalah ini, model rangkaian saraf selular umum dua lapisan dicadangkan untuk pemprosesan imej, di mana dua templat diperkenalkan di antara dua lapisan. Kami mendapati daripada simulasi bahawa CNN dua lapisan berkelakuan cekap berbanding CNN satu lapisan untuk banyak aplikasi pemprosesan imej. Sebagai contoh, masalah simulasi seperti tugasan tidak boleh dipisahkan secara linear--logik XOR, pengesanan titik tengah dan pemisahan objek, dsb. boleh diselesaikan dengan cekap dengan CNN dua lapisan. Masalah kestabilan CNN dua lapisan dengan templat gandingan simetri dan/atau khas juga dibincangkan berdasarkan teknik fungsi Lyapunov. Titik keseimbangannya didapati daripada trajektori dalam satah fasa, yang hasilnya bersetuju dengan hasil daripada simulasi.
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Salinan
Zonghuang YANG, Yoshifumi NISHIO, Akio USHIDA, "Image Processing of Two-Layer CNNs--Applications and Their Stability--" in IEICE TRANSACTIONS on Fundamentals,
vol. E85-A, no. 9, pp. 2052-2060, September 2002, doi: .
Abstract: Cellular Neural Networks (CNNs) have been developed as a high-speed parallel signal-processing platform. In this paper, a generalized two-layer cellular neural network model is proposed for image processing, in which two templates are introduced between the two layers. We found from the simulations that the two-layer CNNs efficiently behave compared to the single-layer CNNs for the many applications of image processing. For examples, simulation problems such as linearly non-separable task--logic XOR, center point detection and object separation, etc. can be efficiently solved with the two-layer CNNs. The stability problems of the two-layer CNNs with symmetric and/or special coupling templates are also discussed based on the Lyapunov function technique. Its equilibrium points are found from the trajectories in a phase plane, whose results agree with those from simulations.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e85-a_9_2052/_p
Salinan
@ARTICLE{e85-a_9_2052,
author={Zonghuang YANG, Yoshifumi NISHIO, Akio USHIDA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Image Processing of Two-Layer CNNs--Applications and Their Stability--},
year={2002},
volume={E85-A},
number={9},
pages={2052-2060},
abstract={Cellular Neural Networks (CNNs) have been developed as a high-speed parallel signal-processing platform. In this paper, a generalized two-layer cellular neural network model is proposed for image processing, in which two templates are introduced between the two layers. We found from the simulations that the two-layer CNNs efficiently behave compared to the single-layer CNNs for the many applications of image processing. For examples, simulation problems such as linearly non-separable task--logic XOR, center point detection and object separation, etc. can be efficiently solved with the two-layer CNNs. The stability problems of the two-layer CNNs with symmetric and/or special coupling templates are also discussed based on the Lyapunov function technique. Its equilibrium points are found from the trajectories in a phase plane, whose results agree with those from simulations.},
keywords={},
doi={},
ISSN={},
month={September},}
Salinan
TY - JOUR
TI - Image Processing of Two-Layer CNNs--Applications and Their Stability--
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2052
EP - 2060
AU - Zonghuang YANG
AU - Yoshifumi NISHIO
AU - Akio USHIDA
PY - 2002
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E85-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 2002
AB - Cellular Neural Networks (CNNs) have been developed as a high-speed parallel signal-processing platform. In this paper, a generalized two-layer cellular neural network model is proposed for image processing, in which two templates are introduced between the two layers. We found from the simulations that the two-layer CNNs efficiently behave compared to the single-layer CNNs for the many applications of image processing. For examples, simulation problems such as linearly non-separable task--logic XOR, center point detection and object separation, etc. can be efficiently solved with the two-layer CNNs. The stability problems of the two-layer CNNs with symmetric and/or special coupling templates are also discussed based on the Lyapunov function technique. Its equilibrium points are found from the trajectories in a phase plane, whose results agree with those from simulations.
ER -