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Open AccessArticle

Series of Semihypergroups of Time-Varying Artificial Neurons and Related Hyperstructures

1
Department of Mathematics, Faculty of Electrical Engineeering and Comunication, Brno University of Technology, Technická 8, 616 00 Brno, Czech Republic
2
Department of Quantitative Methods, University of Defence in Brno, Kounicova 65, 662 10 Brno, Czech Republic
*
Author to whom correspondence should be addressed.
Symmetry 2019, 11(7), 927; https://doi.org/10.3390/sym11070927
Received: 30 May 2019 / Revised: 28 June 2019 / Accepted: 2 July 2019 / Published: 16 July 2019
Detailed analysis of the function of multilayer perceptron (MLP) and its neurons together with the use of time-varying neurons allowed the authors to find an analogy with the use of structures of linear differential operators. This procedure allowed the construction of a group and a hypergroup of artificial neurons. In this article, focusing on semihyperstructures and using the above described procedure, the authors bring new insights into structures and hyperstructures of artificial neurons and their possible symmetric relations. View Full-Text
Keywords: time-varying artificial neuron; ordered group; transposition hypergroup; linear differential operator time-varying artificial neuron; ordered group; transposition hypergroup; linear differential operator
MDPI and ACS Style

Chvalina, J.; Smetana, B. Series of Semihypergroups of Time-Varying Artificial Neurons and Related Hyperstructures. Symmetry 2019, 11, 927.

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