Fuzzy multivariate forecasting of the implementation of regional development programs for the agro-industrial complex

Abstract

The article is devoted to the problems of improving digital intellectual tools for managing the implementation of socio-economic and technological programs aimed at developing the agro-industrial cluster of the regional economy. The aim of the work is to develop a procedure for forecasting the implementation of programs based on the data of the previous stages and knowledge, reflecting the specifics of agricultural production. To describe the indicators of the current and projected state of the regional agro-industrial complex, it is proposed to use the apparatus of the theory of linguistic variables, which makes it possible to use expert technologies for filling the knowledge base and allows us to take into account the high level of uncertainty characteristic of the agricultural market. The links between current and projected performance are represented by fuzzy production rules. The fuzzy inference procedure used in forecasting (based on the Mamdani algorithm) is built in the form of an interpreted fuzzy multilayer neural network. The preliminary results of using the developed procedure as part of a research prototype of an information-analytical system may indicate its effectiveness. The practical significance of the developed toolkit is due to the possibility of its use as a means of intellectual support for making scientifically grounded management decisions on the implementation (taking into account possible adjustments) of development programs for the regional agro-industrial complex.