Neural network modeling of innovative development of JSC «Ukrtransnaft»

Abstract

Listen

Translate article

Methods. The following methods were used in the research: analysis and synthesis – to set the problem of neural network modeling of the level of innovative development of economic entities and the selection of its stages; methods of generalization and grouping – in the formation of a system of individual indicators to determine the level of innovative development; multilayer perceptron training method – to characterize the operation of the neural network. Results. The article considers the possibility of applying the method of forecasting the level of innovative development of enterprises of the oil transportation complex with the use of neural network modeling. The problem of neural network modeling of the level of innovative development of economic entities is set, and its six stages are defined: formation of a system of indicators of the level of innovative development, preliminary data processing, neural network formation, neural network training and result. There has been formed a system of individual indicators for determining the level of innovative development by production, personnel, financial and property components. These indicators are used to build a neural network. There are specific limitations that characterize the learning of the neural network and distinguish it from the general optimization problems: astronomical number of parameters, the need for high parallelism in learning, multicriteria of problems, the need to find an area in which the values of all minimized functions are close to minimum. The advantages and disadvantages of neural network forecasting of the level of innovative development of enterprises are determined. Novelty. An algorithm for forecasting the level of innovative development of oil transportation enterprises using neural network modeling is proposed. A system of individual indicators for determining the level of innovative development by production, personnel, financial and property components has been formed. Practical value. The results of the study have theoretical and practical value, as they allow the use of neural network modeling to predict the level of innovative development of enterprises.