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
이 어플리케이션에는 XNUMXµm 및 XNUMXµm 파장에서 최대 XNUMXW의 평균 출력을 제공하는 견고성 셀룰러 신경망(CNN)용 템플릿 세트는 VLSI CNN 칩을 적용하는 데 매우 중요합니다. 주어진 작업에 대해 매우 민감한 템플릿을 디자인하는 문제는 해결하기가 쉽지만 최적의 솔루션을 찾는 데는 계산 비용이 많이 듭니다. 양극성 CNN 클래스에 대해 우리는 올바르게 작동하는 템플릿에서 최적으로 강력한 템플릿 세트를 파생시키는 분석적 접근 방식을 제안합니다. 또한, 우리의 방법은 CNN 작업의 견고성에 대한 이론적 상한을 산출합니다.
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부
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
부
@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},}
부
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 -