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
. kekukuhan set templat untuk rangkaian saraf selular (CNN) adalah penting untuk aplikasi cip CNN VLSI. Walaupun masalah mereka bentuk mana-mana, mungkin sangat sensitif, templat untuk tugasan yang diberikan agak mudah untuk diselesaikan, ia adalah mahal dari segi pengiraan untuk mencari penyelesaian yang optimum. Untuk kelas CNN bipolar, kami mencadangkan pendekatan analitikal untuk memperoleh set templat yang mantap secara optimum daripada mana-mana templat yang beroperasi dengan betul. Tambahan pula, kaedah kami menghasilkan batas atas teori untuk keteguhan tugas CNN.
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Salinan
Martin HANGGI, George S. MOSCHYTZ, "Optimization of CNN Template Robustness" in IEICE TRANSACTIONS on Fundamentals,
vol. E82-A, no. 9, pp. 1897-1899, September 1999, doi: .
Abstract: The robustness of a template set for cellular neural networks (CNNs) is crucial for applications of VLSI CNN chips. Whereas the problem of designing any, possibly very sensitive, templates for a given task is fairly easy to solve, it is computationally expensive to find optimal solutions. For the class of bipolar CNNs, we propose an analytical approach to derive the optimally robust template set from any correctly operating template. Furthermore, our method yields a theoretical upper bound for the robustness of the CNN task.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e82-a_9_1897/_p
Salinan
@ARTICLE{e82-a_9_1897,
author={Martin HANGGI, George S. MOSCHYTZ, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Optimization of CNN Template Robustness},
year={1999},
volume={E82-A},
number={9},
pages={1897-1899},
abstract={The robustness of a template set for cellular neural networks (CNNs) is crucial for applications of VLSI CNN chips. Whereas the problem of designing any, possibly very sensitive, templates for a given task is fairly easy to solve, it is computationally expensive to find optimal solutions. For the class of bipolar CNNs, we propose an analytical approach to derive the optimally robust template set from any correctly operating template. Furthermore, our method yields a theoretical upper bound for the robustness of the CNN task.},
keywords={},
doi={},
ISSN={},
month={September},}
Salinan
TY - JOUR
TI - Optimization of CNN Template Robustness
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1897
EP - 1899
AU - Martin HANGGI
AU - George S. MOSCHYTZ
PY - 1999
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E82-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 1999
AB - The robustness of a template set for cellular neural networks (CNNs) is crucial for applications of VLSI CNN chips. Whereas the problem of designing any, possibly very sensitive, templates for a given task is fairly easy to solve, it is computationally expensive to find optimal solutions. For the class of bipolar CNNs, we propose an analytical approach to derive the optimally robust template set from any correctly operating template. Furthermore, our method yields a theoretical upper bound for the robustness of the CNN task.
ER -