Intelligent control systems and neural networks

Description: The discipline is devoted to the study of issues related to the construction of intellectual systems for managing complex dynamic objects in the class of multi -level hierarchical systems. The theoretical foundations of building intellectual control systems using fuzzy logic, neural networks are considered. Neway technologies are described in control systems, synthesis of control systems based on fuzzy logic. The main tasks and examples of their solutions in intellectual management systems are considered.

Amount of credits: 5

Пререквизиты:

• Automation of Engineering Systems

Course Workload:

Component: Component by selection

Cycle: Profiling disciplines

Goal

• Preparation of a highly qualified specialist who deeply knows the theory and practice of constructing intellectual control systems based on the latest innovative technologies, which can perform calculations of automation systems with the widespread use of modern computer technology

Objective

• the development of the basic principles of the organization of intellectual management systems, methods and technologies in their use;
• mastering the apparatus of fuzzy logic, the theory of fuzzy sets, neural networks to solve the problems of applied mathematics, as well as the construction and research of the corresponding fuzzy and neural network models of control systems;
• the formation of scientific ideas about the principles and methods of designing, developing and operating intellectual control systems used in the field of automation of technical control systems;
• mastering the ability to organize and conduct experimental research and computer modeling using modern means and methods.

Learning outcome: knowledge and understanding

• describe technologies, methods and means of synthesis of intellectual control systems;

Learning outcome: applying knowledge and understanding

• choose information technologies for building intelligent technical systems and technological process control systems;

Learning outcome: formation of judgments

• analyze data on stability, evaluate the accuracy and quality of intellectual management systems;

Learning outcome: communicative abilities

• apply the results of the development of discipline in professional activities;

Learning outcome: learning skills or learning abilities

• apply the apparatus of fuzzy logic, theory of fuzzy sets, neural networks for building and research of models of intellectual management systems;
• apply a package of Matlab application programs for the research and modeling of intelligent control systems.

Teaching methods

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

building scenarios for the development of various situations based on given conditions;

information and communication (for example, classes in a computer class using professional application software packages);

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

Assessment of the student's knowledge

Teacher oversees various tasks related to ongoing assessment and determines students' current performance twice during each academic period. Ratings 1 and 2 are formulated based on the outcomes of this ongoing assessment. The student's learning achievements are assessed using a 100-point scale, and the final grades P1 and P2 are calculated as the average of their ongoing performance evaluations. The teacher evaluates the student's work throughout the academic period in alignment with the assignment submission schedule for the discipline. The assessment system may incorporate a mix of written and oral, group and individual formats.

The evaluating policy of learning outcomes by work type

Evaluation form

The student's final grade in the course is calculated on a 100 point grading scale, it includes:

• 40% of the examination result;
• 60% of current control result.

The final grade is calculated by the formula:

 

Where Midterm 1, Midterm 2are digital equivalents of the grades of Midterm 1 and 2;

E is a digital equivalent of the exam grade.

Final alphabetical grade and its equivalent in points:

The letter grading system for students' academic achievements, corresponding to the numerical equivalent on a four-point scale:

Topics of lectures

• Intelligent systems and technologies in knowledge engineering
• Goals and objectives of intelligent control
• Intelligent control systems using fuzzy logic
• Fuzzy algorithms
• The procedure for synthesizing fuzzy controllers
• Stability of systems with fuzzy controllers
• Practical examples of constructing intelligent control systems with fuzzy controllers
• Intelligent control systems using neural networks
• Application of neural networks in problems of identification of dynamic objects
• Synthesis of the structure of a multimode neural network controller
• Examples of building neural network systems for controlling dynamic objects
• Software and hardware implementation of neural networks
• The use of intelligent systems and technologies in professional activities: organizing a dialogue between a person and an intelligent system
• Development of complex subject-oriented intelligent systems based on a natural language interface
• Projects with the main objects, processes and phenomena associated with intelligent systems and the use of methods of their scientific research

Key reading

• Stankevich L. A. Intelligent systems and technologies: textbook and workshop for universities. – M.: Yurayt Publishing House, 2023. - 495 p.
• Yasnitsky L.N. Intelligent systems [Electronic resource]: textbook. – M.: Laboratory of Knowledge, 2016. – 224 p. https://archive.org/details/20230506_20230506_0951/page/3/mode/2up
• Vasiliev V.I., Ilyasov B.G. Intelligent control systems. Theory and practice; tutorial. – M.: Radio engineering, 2009. – 392 p.

Further reading

• Polyakov A.E., Ivanov M.S. Fundamentals of the theory of intelligent control of energy-saving modes. – St. Petersburg: Lan, 2022. – 284 p.
• Evmenov V.P. Intelligent control systems: textbook. – M.: Book house “Librokom”, 2009. – 304 p.
• Rutkowska D., Pilinski M., Rutkowski L. Neural networks, genetic algorithms and fuzzy systems. – M.: Hotline-Telecom, 2013. – 384 p.
• Tarasyan, V.S. Fuzzy Logic Toolbox for Matlab package: textbook. allowance / V.S. Tarasyan. – Ekaterinburg: Publishing house UrGUPS, 2013. – 112 p.
• Nikolaeva S.G. Neural networks. Implementation in Matlab: tutorial / S.G. Nikolaev. – Kazan: Kazan. state energy univ., 2015. – 92 p.
• https://dokumen.tips/documents/fuzzy-logic-toolbox-users-guide.html
• https://dokumen.tips/education/neural-network-toolbox-matlab.html