ПРОГНОЗУВАННЯ ВНУТРІШНЬОГО ВАЛОВОГО ПРОДУКТУ НА ОСНОВІ АПАРАТУ ШТУЧНИХ НЕЙРОННИХ МЕРЕЖ
The aim of this research is to develop a new approach to forecasting GDP by means of neural network algorithm based on real data. During the study used a system of economic-statistical and economic-mathematical methods. In particular, use of the method of mathematical modeling on the basis of artificial neural networks, which include structural modeling and methods of teaching based on the developed theory of nonlinear programming. Using the main types of tasks for forecasting GDP. Implemented to generate datasets for one-parameter problem by the method of "time window". The proposed system of economic-mathematical models based on the using of artificial neural networks algorithm for forecasting GDP. Using the new approach to forecasting GDP on the basis of application of artificial neural networks algorithm, will give opportunity to provide GDP change in the future and will help to identify the degree of influence of one or another factor, which will influence the increase or decrease of the country''s GDP, and will gave option of regulation not only GDP, but also other macroeconomic indicators which connected with GDP. The new approach to forecasting GDP is building of the neural network model, solved the main types of tasks for forecasting GDP by using of artificial neural networks algorithm, which can most effectively make predictions in the future and to implement adequate mechanisms of state influence on the GDP dynamics, as control of the growth or decline in GDP is the key problem of the country''s economy. The analysis proves that GDP forecasting is necessary to evaluate the most important macroeconomic parameters, forecasting can be done by constructing a neural network model, which will done the most effectively projection in the future and will allow to implement adequate mechanisms of state influence on the GDP level, as control of the growth or decline in GDP is the key problem of the country''s economy.