Fuzzy algorithms and controls

Description: The discipline addresses the following issues: Fundamentals of fuzzy sets. Fundamentals of fuzzy algorithms. Linguistic variable. Fuzzy trapezoid. Membership function The use of fuzzy approaches in applied modeling. Fuzzy approach to the management of automatic systems. Fuzzy approach to modeling quality management processes. Fuzzy models of quality assessment process control. Students will gain skills to describe fuzzy concepts and knowledge.

Amount of credits: 5

Пререквизиты:

• Adaptive methods of prediction of technological process parameters

Course Workload:

Component: University component

Cycle: Profiling disciplines

Goal

• the study of the modern Fuzzy Logic Control Systems in industry and other spheres of life based on the methods of mathematical and computer modeling, the development of the theoretical foundations, for the development of devices and automation systems, obtaining skills in solving engineering problems of complex technical systems to automate through modern technologies for automation

Objective

• Study of fuzzy control theory
• Study of fuzzy control algorithms
• Formation of the ability to develop fuzzy process control systems in automation systems for technical objects
• Formation of skills for solving fuzzy control problems

Learning outcome: knowledge and understanding

• demonstrate developing knowledge and understanding obtained at the level of higher professional education, which are the basis or opportunity for the original development or application of ideas, often in the context of scientific research

Learning outcome: applying knowledge and understanding

• apply knowledge, understanding and ability to solve problems in new or unfamiliar situations in the context and framework of wider (or interdisciplinary) areas related to the field of automation and control

Learning outcome: formation of judgments

• the ability to independently apply the methods and means of cognition, training and self-control, to realize the prospects of intellectual, cultural, moral, physical and professional self-development and self-improvement, to be able to critically assess their strengths and weaknesses.

Learning outcome: communicative abilities

• willingness to change social, economic, professional roles, geographical and social mobility in the context of the dynamics of change, continue to study independently

Learning outcome: learning skills or learning abilities

• carry out communications in the professional sphere and in society as a whole, including in a foreign language, analyze existing and develop technical documentation independently, clearly state and protect the results of complex engineering activities in the field of automation and control

Teaching methods

interactive lecture (use of the following active forms of learning: guided (managed) discussion or conversation; moderation; demonstration of slides or educational films; brainstorming;

search and research (independent research activities of students during the learning process);

Topics of lectures

• On methods for constructing membership functions of fuzzy sets
• Fuzzy numbers of (L-R) -type
• Logical-linguistic description of systems, fuzzy models
• Fuzzy modeling methods
• Fuzzy control

Key reading

• Bronevich, A. G., Lepskij, A. E. Nechetkie modeli analiza dannyh i prinyatiya reshenij : uchebnoe posobie / A. G. Bronevich, A. E. Lepskij; Nac. issled. un-t «Vysshaya shkola ekonomiki». - M.: Izd. dom Vysshej shkoly ekonomiki, 2022. - 264 s.
• Pegat A. Nechetkoe modelirovanie i upravlenie / A. Pegat ; per. s angl. - 2-e izd. - M. : BINOM. Laboratoriyaznanij, 2013. - 798 s.

Further reading

• Kruglov, V. V. Nechetkaya logika i iskusstvennye nejronnye seti / V. V. Kruglov, M. I. Dli, R. Yu. Golunov. – M. : FIZMATLIT, 2001. – 224 s.
• Paklin, N. Nechetkaya logika – matematicheskie osnovy [Elektronnyj resurs] / N. Paklin. – Rezhim dostupa: http://www.basegroup.ru/library/analysis/fuzzylogic/math/