Synthesis of neural network based on Petry net using as example the problem of movement and stabilization of structure groups of unmanned aerial vehicles

The article focuses on the approach to forming the structure of a neural network with application of a pre-built algorithm using Petri nets which represent a well-characterized body of mathematics and help to describe algorithms, in particular, distributed asynchronous systems. According to the proposed approach, the model built in Petri nets serves as the basis for further developing the neural network. There was proposed the idea of informal transformation, which makes sense because the structure of Petri net provides substantiation for the structure of the neural network. This fact leads to decreasing the number of training parameters in the neural network (in the example described in the article the decrease was more than twice: from 650 to 254), increasing the time of the network training and getting initial values for the training parameters. It has been stated that with the initial values obtained the training time grows even more and, thus, training process acts as fine-adjusting values of parameters. Transformation can be explained by the fact that both Petri nets and neural networks act as languages for describing functions, and differ only in the case of neural networks, where the presented function must be trained first (or to find parameter values). The above-mentioned approach is illustrated by the example of the problem of automatic formation of a group of unmanned aerial vehicles (UAV) and their movement. In this problem, identical instances of the neural network are located on each UAV and interact in asynchronous mode.