  In this article, we propose the approach to procedural optimization of a
neural network, based on the combination of information theory and braid
theory. The network studied in the article implemented with the intersections
between the braid strands, as well as simplified networks (a network with
strands without intersections and a simple convolutional deep neural network),
are used to solve various problems of multiclass image classification that
allow us to analyze the comparative effectiveness of the proposed architecture.
The simulation results showed BraidNet's comparative advantage in learning
speed and classification accuracy.
