<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The article deals with the artificial multilayer neural network with direct signal transmission and reverse error propagation, which operates in the mode of identification of static and dynamic characteristics of the electric drive. To study processes of nearly identical, nanoprene of neuroprogenitor has been developed by the research stand, which is described in the article. As a result of research on the stand, the results show that the neural network is well enough to identify the static and dynamic characteristics of the electric drive.</p></abstract>

The article deals with the artificial multilayer neural network with direct signal transmission and reverse error propagation, which operates in the mode of identification of static and dynamic characteristics of the electric drive. To study processes of nearly identical, nanoprene of neuroprogenitor has been developed by the research stand, which is described in the article. As a result of research on the stand, the results show that the neural network is well enough to identify the static and dynamic characteristics of the electric drive.

Study of the Processes of Near Identical, Nanoprene, Neuro Progenitor Electric Drive

Latvian Journal of Physics and Technical Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

The article deals with the artificial multilayer neural network with direct signal transmission and reverse error propagation, which operates in the mode of...

Study of the Processes of Near Identical, Nanoprene, Neuro Progenitor Electric Drive

Publié en ligne: 28 mars 2019

Pages: 29 - 40

DOI: https://doi.org/10.2478/lpts-2019-0003

Mots clés
clustering, drive, materials science, neural network, training

© 2019 Viktor M. Buyankin, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Study of the Processes of Near Identical, Nanoprene, Neuro Progenitor Electric Drive

Publié en ligne: 28 mars 2019

Pages: 29 - 40

DOI: https://doi.org/10.2478/lpts-2019-0003

Mots clésclustering, drive, materials science, neural network, training

© 2019 Viktor M. Buyankin, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Mots clés
clustering, drive, materials science, neural network, training