DOI 10.15507/2079-6900.26.202402.111-122
Original article
ISSN 2079-6900 (Print)
ISSN 2587-7496 (Online)
MSC2020 08A62
About One Groupoid Associated with the Composition of Multilayer Feedforward Neural Networks
A. V. Litavrin, T. V. Moiseenkova
Siberian Federal University, (Krasnoyarsk, Russian Federation)
Abstract. The authors construct a groupoid whose elements are associated with multilayer feedforward neural networks. This groupoid is called the complete groupoid of the composition of neural networks. Multilayer feedforward neural networks (hereinafter referred to as neural networks) are modelled by defining a special type of tuple. Its components define layers of neurons and structural mappings that specify weights of synaptic connections, activation functions and threshold values. Using the artificial neuron model (that of McCulloch-Pitts) for each such tuple it is possible to define a mapping that models the operation of a neural network as a computational circuit. This approach differs from defining a neural network using abstract automata and related constructions. Modeling neural networks using the proposed method makes it possible to describe the architecture of the network (that is, the network graph, the synaptic weights, etc.). The operation in the full neural network composition groupoid models the composition of two neural networks. A network, obtained as the product of a pair of neural networks, operates on input signals by sequentially applying original networks and contains information about their structure. It is proved that the constructed groupoid is a free.
Key Words: groupoid, free groupoid, multilayer feedforward neural network, complete groupoid composition of multilayer neural networks
For citation: A. V. Litavrin, T. V. Moiseenkova. About One Groupoid Associated with the Composition of Multilayer Feedforward Neural Networks. Zhurnal Srednevolzhskogo matematicheskogo obshchestva. 26:2(2024), 111–122. DOI: https://doi.org/10.15507/2079-6900.26.202402.111-122
Submitted: 13.03.2024; Revised: 30.04.2024; Accepted: 29.05.2024
Information about the authors:
Andrey V. Litavrin, Ph.D. (Phys.-Math.), Associate Professor, Associate Professor of the Department of Higher Mathematics No. 2, Siberian Federal University (79 Svobodny Av., Krasnoyarsk 660041, Russia), ORCID: https://orcid.org/0000-0001-6285-0201, anm11@rambler.ru
Tatyana V. Moiseenkova, Ph.D. (Phys.-Math.), Associate Professor of the Department of Higher Mathematics No. 2, Siberian Federal University (79 Svobodny Av., Krasnoyarsk 660041, Russia), ORCID: https://orcid.org/0009-0009-2216195X, tanya-mois11@yandex.ru
All authors have read and approved the final manuscript.
Conflict of interest: The authors declare no conflict of interest.