The System of Automated Development, Learning and Execution of Artificial Neural Networks

Abstract

Goal: the need for systems of automated generation of models of complexly formalized objects is considered. The approach to the creation of such a system based on deep learning is described. Materials and methods: the article describes the architecture of the application of automated learning, based on deep learning, in particular on the basis of the genetic algorithm. Results: the testing of the presented system was carried out on the example of solving the problem of predicting the parameters of ice drift on the Northern Dvina River. Conclusion: the advantages and disadvantages, features of implementation, the scope of the presented system are shown.