IMPACT OF DEFECTS ON MULTILAYER ARTIFICIAL FEEDFORWARD NEURAL NETWORK OPERABILITY
Abstract
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.
About the Authors
Daniil V. Marshakov
Don State Technical University
Russian Federation
Russian Federation
Vladimir A. Fatkhi
Don State Technical University
Russian Federation
Russian Federation
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Review
For citations:
Marshakov D.V., Fatkhi V.A. IMPACT OF DEFECTS ON MULTILAYER ARTIFICIAL FEEDFORWARD NEURAL NETWORK OPERABILITY. Vestnik of Don State Technical University. 2011;11(2):169-173. (In Russ.)
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