Fuzzy regression modeling in tasks of management of agrarian production

The problem of accounting for and modeling uncertainty in the modern tasks of management is one of the most pressing. The efficacy of decisions depends significantly on the methods of description available in the challenge of uncertainty. The greatest development in agricultural science has received optimization and econometric models. However, they both are based on quantitative deterministic baseline information and consideration uncertainty as randomness, for which apply probabilistic and statistical methods. Meanwhile, many modern decision-making tasks in the planning and management of agricultural production are characterized by the presence of indeterminate factors and the availability of high-quality, inaccurate or incomplete information. To account for and describe this uncertainty needs probabilistic approach an alternative approach. One of the most effective mathematical tools aimed at formalizing and processing of uncertain information are the methods of theory of fuzzy sets. Section econometrics associated with the use of fuzzy set theory in regression analysis develops the meth-ods of fuzzy regression modeling. This article discusses the application possibilities of the fuzzy regression modeling for analysis of management processes in agricultural production

Keywords: fuzzy modeling, regression analysis, agricultural production


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