Convolutional neural networks training for autonomous robotics

• Autorzy: Lozhkin Alexander , Maiorov Konstantin , Bozek Pavol

Identyfikatory

DOI

10.2478/mspe-2021-0010

• Języki publikacji: EN

Abstrakty

EN

The article discusses methods for accelerating the operation of convolutional neural networks for autonomous robotics learning. The analysis of the theoretical possibility of modifying the neural network learning mechanism is carried out. Classic semiotic analysis and the theory of neural networks is proposed to union. An assumption is made about the possibility of using the symmetry mechanism to accelerate the training of convolutional neural networks. A multilayer neural network to represent how space is an attempt has been made. The conclusion was based on the laws on the plane obtained earlier. The derivation of formulas turned out to be impossible due to the problems of modern mathematics. A new approach is proposed, which involves combining the gradient descent algorithm and the stochastic completion of convolutional filters by the principles of symmetries. The identified algorithms allow increasing the learning rate from 5% to 15%, depending on the problem that the neural network solves.

Słowa kluczowe

EN

autonomous robots; convolutional neural networks; learning; autoromorphisms; symmetry mechanism; semiotic analyze

• Wydawca: STE GROUP
• Czasopismo: Management Systems in Production Engineering
• Rocznik: 2021
• Tom: nr 1 (29)
• Strony: 75--79
• Opis fizyczny: Bibliogr. 28 poz., rys.

Twórcy

autor

Lozhkin Alexander

• Kalashnikov Izhevsk State Technical University Institute of Informatics and Hardware Software department Student Street, 7, Izhevsk, Russia

• https://orcid.org/0000-0001-9048-2469

autor

Maiorov Konstantin

• Slovak University of Technology Faculty of Materials Science and Technology Institute of Production Technologies J. Bottu 25, 917 24 Trnava, Slovak Republic

• https://orcid.org/0000-0002-3891-3847

autor

Bozek Pavol

• Kalashnikov Izhevsk State Technical University Institute of Informatics and Hardware Software department Student Street, 7, Izhevsk, Russia

• https://orcid.org/0000-0003-4285-7697

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).