IMPACT OF DEFECTS ON MULTILAYER ARTIFICIAL FEEDFORWARD NEURAL NETWORK OPERABILITY

IMPACT OF DEFECTS ON MULTILAYER ARTIFICIAL FEEDFORWARD NEURAL NETWORK OPERABILITY

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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 2018-07-01
Series:	Вестник Донского государственного технического университета
Subjects:	fault tolerance feedforward neural network artificial recognition indices of correct recognition.
Online Access:	https://vestnik.donstu.ru/jour/article/view/706

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