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
내구성은 불완전한 조건에서도 장치가 제대로 작동할 수 있는 능력을 나타냅니다. 우리는 최근 AfNN(Affordable Neural Network)이라는 새로운 신경망 구조를 제안했습니다. 여기서는 은닉층의 저렴한 뉴런을 인간의 뇌 기능에서 관찰되는 견고성 특성을 담당하는 요소로 간주합니다. 앞서 우리는 AfNN이 여전히 일반화하고 학습할 수 있다는 것을 보여주었지만, 여기서는 이러한 네트워크가 학습 프로세스가 종료된 후에 발생하는 손상에 대해 강력하다는 것을 보여줍니다. 결과는 AfNN이 내구성이라는 중요한 특징을 구현한다는 견해를 뒷받침합니다. 우리의 기여에서 우리는 학습 과정 후에 숨겨진 계층의 일부 뉴런이 손상되었을 때 AfNN의 내구성을 조사했습니다.
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부
Yoko UWATE, Yoshifumi NISHIO, Ruedi STOOP, "Durability of Affordable Neural Networks against Damaging Neurons" in IEICE TRANSACTIONS on Fundamentals,
vol. E92-A, no. 2, pp. 585-593, February 2009, doi: 10.1587/transfun.E92.A.585.
Abstract: Durability describes the ability of a device to operate properly in imperfect conditions. We have recently proposed a novel neural network structure called an "Affordable Neural Network" (AfNN), in which affordable neurons of the hidden layer are considered as the elements responsible for the robustness property as is observed in human brain function. Whereas earlier we have shown that AfNNs can still generalize and learn, here we show that these networks are robust against damages occurring after the learning process has terminated. The results support the view that AfNNs embody the important feature of durability. In our contribution, we investigate the durability of the AfNN when some of the neurons in the hidden layer are damaged after the learning process.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E92.A.585/_p
부
@ARTICLE{e92-a_2_585,
author={Yoko UWATE, Yoshifumi NISHIO, Ruedi STOOP, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Durability of Affordable Neural Networks against Damaging Neurons},
year={2009},
volume={E92-A},
number={2},
pages={585-593},
abstract={Durability describes the ability of a device to operate properly in imperfect conditions. We have recently proposed a novel neural network structure called an "Affordable Neural Network" (AfNN), in which affordable neurons of the hidden layer are considered as the elements responsible for the robustness property as is observed in human brain function. Whereas earlier we have shown that AfNNs can still generalize and learn, here we show that these networks are robust against damages occurring after the learning process has terminated. The results support the view that AfNNs embody the important feature of durability. In our contribution, we investigate the durability of the AfNN when some of the neurons in the hidden layer are damaged after the learning process.},
keywords={},
doi={10.1587/transfun.E92.A.585},
ISSN={1745-1337},
month={February},}
부
TY - JOUR
TI - Durability of Affordable Neural Networks against Damaging Neurons
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 585
EP - 593
AU - Yoko UWATE
AU - Yoshifumi NISHIO
AU - Ruedi STOOP
PY - 2009
DO - 10.1587/transfun.E92.A.585
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E92-A
IS - 2
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - February 2009
AB - Durability describes the ability of a device to operate properly in imperfect conditions. We have recently proposed a novel neural network structure called an "Affordable Neural Network" (AfNN), in which affordable neurons of the hidden layer are considered as the elements responsible for the robustness property as is observed in human brain function. Whereas earlier we have shown that AfNNs can still generalize and learn, here we show that these networks are robust against damages occurring after the learning process has terminated. The results support the view that AfNNs embody the important feature of durability. In our contribution, we investigate the durability of the AfNN when some of the neurons in the hidden layer are damaged after the learning process.
ER -