Infological modeling of information systems subject industries in solving of fuzzy control tasks

DOI №______

Abstract

The development of information technologies for solving problems of fuzzy control requires the use of tools for infological modelling. Existing approaches for describing the tasks of fuzzy control based on the fact that the control unit, as a rule, is represented by a rectangle, and to the left and to the right of it there are arrows indicating the incoming and outgoing information. Existing methodologies for infological modelling, which include IDEF, UML, DFD, ER, Fuzzy IFO and others, are focused primarily on the description of processes, data structures and classes and do not take into account the specifics of fuzzy control problems. In this paper, approaches for the creation of a graphical conceptual model for the problem of fuzzy control, models of linguistic variables and the structure of knowledge base rules in the form of fuzzy products are proposed. In the conceptual model of the fuzzy control problem, a list of input and output variables of the control unit, corresponding linguistic variables and the connections between them are given. In addition, the model specifies the restrictions of the control task. The model of a linguistic variable contains detailed information about it in accordance with the classical definition. The rule structure model of the data-base of fuzzy inference clearly reflects the links between antecedents and consequents. The using of these models makes it possible to provide information on the formulation of the fuzzy control problem in terms of the subject area, which provides an increase in the effectiveness of its consideration. The results of the work can be used to describe the subject areas in the development of information technology for automating the management tasks of complex systems.

Keywords: information technology; infological modelling; fuzzy control.

Refrences
1. Zadeh L. A. Fuzzy sets // Information and Control. 1965. Vol. 8. P. 338–353.
2. Ma Z. M., Li Yan. A Literature Overview of Fuzzy Conceptual Data Modeling // Journal of information science and engineering. 2010. № 26. С. 427–441.
3. Peter Pin-Shan Chen. The Entity-Relationship Model – Toward a Unified View of Data // ACM Transactions on Database Systems (TODS). 1976. Vol. 1. P. 9–36.
4. Буч Г., Рамбо Д., Якобсон И. Краткая история UML // Язык UML. Руководство пользователя = The Unified Modeling Language Usere Guide. 2006. С. 14. 496 с.
5. Дубовик С. Е., Сошников Д. В. Использование семантических сетей, расширенных деревьями И/ИЛИ для представления структурно-динамических знаний в интеллектуальных системах // Электронный журнал Труды МАИ. 2003. № 13.
6. Леоненков А. В. Нечеткое моделирование в среде MATLAB и fuzzyTECH. 2005. 736 с.
7. Лямец В. И. Основы общей теории систем и системный анализ. 2015. 304 с.
8. Маклаков С. В. Создание информационных систем с AllFusion Modelling Suite. 2003. 432 с.
9. Шушура О. М., Тарасова І. О. Метод нечеткого управления на основе переменных с многомерными функциями принадлежности // Штучний інтелект. 2010. № 1. С. 122–128.
10. Шушура А. Н., Тарасова И. А. Cинтез структуры и алгоритмов системы нечеткого управления с использованием функций принадлежности нескольких аргументов // Universum: техн. науки: электрон. науч. журн. 2014. № 4(5). URL: http://7universum.com/ru/tech/archive/item/1259