Using neural network for structure optimization of scientific-educational center of world-class level

The article describes theoretical basis of using machine learning, especially neural networks to optimise modelling process of world-class scientific and educational centers creation. The authors analyzed the machine learning process: cognitive recognition system, cognitive decision-making system, cognitive informatics, cognitive robotics. The options were identified for the application of machine learning methods in the process of solving the problem of optimizing the organisational structure of Samara region innovation activities stimulation and development. In addition, the article highlights some decision-making approaches in the process of creating a scientific and educational center which is based in region with regional main participants and setting the goal of reaching the world level. The results of the analysis showed the potential of the Samara-Tolyatti agglomeration to create a scientific and educational center, identified the main areas of activity of it. As a result of the study, a mechanism was identified for bringing technology and research to world markets. A structure was built, and the process of development of the environment of the research and education center was determined. The authors identified the experience and achievements of scientific schools as the basis for the development of the scientific and educational center, while dividing them into three levels: regional, federal and world-class. The results of the study are confirmed by approved regulatory legal acts.