Neural Network “Perseptron” and a Semantic Model of Knowledge Representation for Studying the Dynamics of the Rf GDP

Abstract

Theoretical foundations of GDP dynamics under conditions of market uncertainty and the formation of a digital economy are studied. Forecasting GDP is important because it allows to ensure balance, sustainability in the development of various sectors of the national economy, and its economic security, which determines the practical significance of the study.
 Purpose. To put forward and prove the hypothesis that with the help of the neural network model “perceptron” and the semantic model of knowledge representation, it is possible to obtain a forecast of Russia’s GDP for the next year.
 The scientific novelty lies in the fact that forecasts of the RF GDP were generated using a semantic knowledge representation model and the Perceptron AI system.
 Method or methodology of the work. The work used such methods as monographic, analytical, artificial intelligence system - perceptron, Mind histogram, as well as comparative analysis. Calculations were performed in tables XL perceptron was formed on the Deductor platform. We used a semantic knowledge representation model developed in the DOT language using the Graphviz 2.38 program.
 Results. Theoretical foundations of the use of neural networks in economic forecasts have been studied. A semantic model of knowledge representation has been formed regarding forecasting the GDP of the Russian Federation by a neural network. The developed AI-system “perceptron” is presented, designed to forecast Russia’s GDP based on input parameters representing a set of data reflecting the development of the real sector of the economy and the financial sector of Russia for 2011–2021.
 The scope of the results: economics, the financial sector, forecasting and planning of economic and financial activities.