ОНТОЛОГІЯ ЯК ПРОГРАМНА НАДБУДОВА ДО СИСТЕМИ ДЛЯ МАТЕМАТИЧНОГО МОДЕЛЮВАННЯ НА ОСНОВІ ІНТЕРВАЛЬНИХ ДАНИХ

Abstract

The article considers an important scientific problem of developing methods and means of constructing discrete models of complex objects in the form of interval difference equations based on a combination of ontological approach and analysis of interval data to expand the scope and conditions of application of models. impetus for the development of applied research in the fields of national defense, environmental protection, medicine and other areas, where the necessary component of the decision support system are mathematical models of objects with distributed parameters. The essence of the approach to mathematical modeling based on interval analysis is characterized, the main feature of which is the multiple estimation of the parameters of the input-output model, built on the results of experiments in which the output variables are obtained in interval form. The main research results presented in the article are: description of the approach to the use of mathematical modeling ontology based on interval data for software development and use, in order to expand the scope and conditions of application of models while ensuring its given prognostic properties; a step-by-step scheme of the process of developing an onto-controlled software system of mathematical modeling based on interval analysis is proposed; the scheme of the process of realization, use and updating of the considered ontological model of the subject area of mathematical modeling on the basis of interval data is offered. The peculiarity of the approaches proposed in this article is that they can be implemented as a software add-on to applied systems of mathematical modeling based on interval analysis. The combination of approaches based on interval analysis and ontological representation of the subject area provides increased efficiency of computational procedures for identifying models of complex objects, as well as the adaptive use of different models for different subject areas in decision support systems.