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Статья
Crystallochemical Design of Huntite-Family Compounds

Kuz’micheva G. M., Kaurova I. A., Rybakov V. B. et al.

Crystals. 2019. Vol. 9. No. 2. P. 1-49.

Глава в книге
Structural Synthesis of the IoT System for the Fog Computing

Saksonov E., Leokhin Y., Panfilov, P.

In bk.: 24th Conference of Open Innovations Association FRUCT, FRUCT 2019. IEEE Computer Society, 2019. P. 381-387.

Препринт
Темпоральные расширения в стандарте SQL

С.Д. Кузнецов

Препринты ИСП РАН. Институт системного программирования им. В.П. Иванникова РАН, 2017. № 30.

Нечеткое моделирование

Руководитель семинара

Аннотация

In everyday life we ​​are constantly confronted with the need to process qualitative information that is often incomplete, inaccurate, and exhibits a lack of specific details. In spite of all this, humans as universal systems, tending to the so-called "Soft Computing", are able to understand and present (at some level) such information, build certain judgments and draw conclusions. In this regard, fuzzy logic gives albeit approximate, but rather practical tool for the formal representation and processing of information used in the description of various systems. As a result, fuzzy logic opens new interesting possibilities for application in different fields of human activity (area of IT and computer/software engineering are not an exception).

Scientific and research seminar (elective course) ‘Fuzzy modeling’ (рус. Нечеткоем оделирование’ ) for undergraduate students (Bachelor degree level) at the Department of Software Engineering is focused on a rather detailed presentation, explanation and discussion of the practical use of basic theory of fuzzy concepts, which cover fuzzy sets and operations on them, linguistic variables, representation of fuzzy knowledge (e.g. fuzzy "if-then" rules), fuzzy relations, etc. Examples of application of fuzzy sets and logic in problems related to planning, classification, forecasting, system analysis and decision-making are considered. In addition, material also covers type-2 fuzzy sets ("fuzzy fuzzy sets") and systems as well as recently introduced Z-numbers that attract much attention of researchers in these latter days.

 

Learning Outcomes:
(1) formation of professional competencies related to both general methodology of scientific research and specific aspects of systems modeling (as applied to systems of different nature) based on fuzzy sets and fuzzy logic,
 
(2) acquisition of skills to work (read, understand, analyze, draw conclusions) with scientific publications (articles, chapters of books, preprints, reports) published in both English and Russian, conduct self-dependent research related to the development, analysis and software implementation of fuzzy models (in particular, with the use of software packages / development environment and/or programming languages).