IMPACT OF DEFECTS ON MULTILAYER ARTIFICIAL FEEDFORWARD NEURAL NETWORK OPERABILITY

Impact of buried layer likely defects on the performance of the multilayer feedforward artificial neural network is investigated. Dimensions of estimation of the correct network operation by pattern recognition are offered.

Bibliographic Details
Main Authors:	Daniil V. Marshakov, Vladimir A. Fatkhi
Format:	Article
Language:	Russian
Published:	Don State Technical University 2011-03-01
Series:	Advanced Engineering Research
Subjects:	fault tolerance feedforward neural network artificial recognition indices of correct recognition.
Online Access:	https://www.vestnik-donstu.ru/jour/article/view/706

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spelling	doaj-e096a3dbf6c34278a95394a2354c700d2021-10-02T18:34:47ZrusDon State Technical UniversityAdvanced Engineering Research2687-16532011-03-01112169173698IMPACT OF DEFECTS ON MULTILAYER ARTIFICIAL FEEDFORWARD NEURAL NETWORK OPERABILITYDaniil V. Marshakov0Vladimir A. Fatkhi1Don State Technical UniversityDon State Technical UniversityImpact of buried layer likely defects on the performance of the multilayer feedforward artificial neural network is investigated. Dimensions of estimation of the correct network operation by pattern recognition are offered.https://www.vestnik-donstu.ru/jour/article/view/706fault tolerancefeedforward neural networkartificial recognitionindices of correct recognition.
collection	DOAJ
language	Russian
format	Article
sources	DOAJ
author	Daniil V. Marshakov Vladimir A. Fatkhi
spellingShingle	Daniil V. Marshakov Vladimir A. Fatkhi IMPACT OF DEFECTS ON MULTILAYER ARTIFICIAL FEEDFORWARD NEURAL NETWORK OPERABILITY Advanced Engineering Research fault tolerance feedforward neural network artificial recognition indices of correct recognition.
author_facet	Daniil V. Marshakov Vladimir A. Fatkhi
author_sort	Daniil V. Marshakov
title	IMPACT OF DEFECTS ON MULTILAYER ARTIFICIAL FEEDFORWARD NEURAL NETWORK OPERABILITY
title_short	IMPACT OF DEFECTS ON MULTILAYER ARTIFICIAL FEEDFORWARD NEURAL NETWORK OPERABILITY
title_full	IMPACT OF DEFECTS ON MULTILAYER ARTIFICIAL FEEDFORWARD NEURAL NETWORK OPERABILITY
title_fullStr	IMPACT OF DEFECTS ON MULTILAYER ARTIFICIAL FEEDFORWARD NEURAL NETWORK OPERABILITY
title_full_unstemmed	IMPACT OF DEFECTS ON MULTILAYER ARTIFICIAL FEEDFORWARD NEURAL NETWORK OPERABILITY
title_sort	impact of defects on multilayer artificial feedforward neural network operability
publisher	Don State Technical University
series	Advanced Engineering Research
issn	2687-1653
publishDate	2011-03-01
description	Impact of buried layer likely defects on the performance of the multilayer feedforward artificial neural network is investigated. Dimensions of estimation of the correct network operation by pattern recognition are offered.
topic	fault tolerance feedforward neural network artificial recognition indices of correct recognition.
url	https://www.vestnik-donstu.ru/jour/article/view/706
work_keys_str_mv	AT daniilvmarshakov impactofdefectsonmultilayerartificialfeedforwardneuralnetworkoperability AT vladimirafatkhi impactofdefectsonmultilayerartificialfeedforwardneuralnetworkoperability
_version_	1716848908659851264

IMPACT OF DEFECTS ON MULTILAYER ARTIFICIAL FEEDFORWARD NEURAL NETWORK OPERABILITY

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