Formation of Weighting Coefficients in an Artificial Neural Network Based on the Memristive Effect in Metal–Oxide–Metal Nanostructures
- Authors: Antonov I.N.1, Belov A.I.1, Mikhaylov A.N.1, Morozov O.A.1, Ovchinnikov P.E.1
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Affiliations:
- Lobachevsky State University of Nizhny Novgorod
- Issue: Vol 63, No 8 (2018)
- Pages: 950-957
- Section: Novel Radio Systems and Elements
- URL: https://journals.rcsi.science/1064-2269/article/view/200091
- DOI: https://doi.org/10.1134/S106422691808003X
- ID: 200091
Cite item
Abstract
An approach to formation and training of an artificial neural network (ANN) based on thin-film memristive metal–oxide–metal nanostructures, which exhibit the effect of bipolar resistive switching, has been proposed. An experimental electric circuit of a small-sized ANN (a two-layer perceptron with 32 memristive elements) has been constructed. An algorithm for formation of weighting coefficients (ANN training), which takes into account probable spread of technological parameters of memristive structures has been developed.
About the authors
I. N. Antonov
Lobachevsky State University of Nizhny Novgorod
Email: oa_morozov@nifti.unn.ru
Russian Federation, Nizhny Novgorod, 603950
A. I. Belov
Lobachevsky State University of Nizhny Novgorod
Email: oa_morozov@nifti.unn.ru
Russian Federation, Nizhny Novgorod, 603950
A. N. Mikhaylov
Lobachevsky State University of Nizhny Novgorod
Email: oa_morozov@nifti.unn.ru
Russian Federation, Nizhny Novgorod, 603950
O. A. Morozov
Lobachevsky State University of Nizhny Novgorod
Author for correspondence.
Email: oa_morozov@nifti.unn.ru
Russian Federation, Nizhny Novgorod, 603950
P. E. Ovchinnikov
Lobachevsky State University of Nizhny Novgorod
Email: oa_morozov@nifti.unn.ru
Russian Federation, Nizhny Novgorod, 603950