Inteligent Control Systems

Објавено: October 12, 2018
  1.    Course Title Inteligent Control Systems
  2.    Code 3ФЕИТ01Л006
  3.    Study program KSIAR, KTI
  4.    Organizer of the study program (unit, institute, department) Faculty of Electrical Engineering and Information Technologies
  5.    Degree (first, second, third cycle) First cycle
  6.    Academic year/semester III/6, IV/8   7.    Number of ECTS credits 6.00
  8.    Lecturer Dr Vesna Ojleska Latkoska
  9.    Course Prerequisites

10.    Course Goals (acquired competencies):  Students completing this course will obtain a basic understanding of fuzzy logic systems and artificial neural networks, and will know how these techniques are applied to engineering problems, including control systems. Students will understand the advantages and disadvantages of these methods relative to other control methods. Students will be aware of current research trends and issues. Students will be able to design control systems using fuzzy logic and artificial neural networks.

11.    Course Syllabus: Introduction to intelligent techniques in control systems: fuzzy logic; neural networks; techniques based on evolution (genetic algorithms); hybrid fuzzy neural systems. Mathematical basis of fuzzy logic. Fuzzy logic and approximate reasoning. Fuzzy inference system. Fuzzification and defuzzification. Mathematical representation of fuzzy logic systems: Mamdani, Takagi-Sugeno and other types of fuzzy models. Fuzzy logic control. Derivative based optimization and their application in fuzzy-neural systems. Derivative free optimization (genetic algorithms) and their applications in fuzzy-neural systems. Adaptive neural networks. Supervised learning neural networks. Identification with fuzzy-neural systems. Fuzzy-neural control systems. Advanced applications of fuzzy-neural systems.
12.    Learning methods:  Combined way of learning: lectures, supported by presentations, homework and auditory exercises, as well as practical laboratory exercises.
13.    Total number of course hours 2 + 2 + 1 + 0
14.    Distribution of course hours 180
15.    Forms of teaching 15.1. Lectures-theoretical teaching 30
15.2. Exercises (laboratory, practice classes), seminars, teamwork 45
16.    Other course activities 16.1. Projects, seminar papers 10
16.2. Individual tasks 10
16.3. Homework and self-learning 85
17.    Grading 17.1. Exams 5
17.2. Seminar work/project (presentation: written and oral) 10
17.3. Activity and participation 5
17.4. Final exam 80
18.    Grading criteria (points) up to 50 points     5 (five) (F)
from 51 to 60 points     6 (six) (E)
from 61 to 70 points     7 (seven) (D)
from 71 to 80 points     8 (eight) (C)
from 81 to 90 points     9 (nine) (B)
from 91 to 100 points   10 (ten) (A)
19.    Conditions for acquiring teacher’s signature and for taking final exam

Regular attendance to the lectures and exercises, as well as successful and timely completion of all laboratory exercises.
20.  Forms of assessment

Two partial written exams are scheduled (at the middle and at the end of the semester, each with duration of 120 minutes), tests that are conducted during the classes, as well as homework and project assignments, which are presented/defended during the semester.
1. Students who have passed the partial exams are considered to have passed the final written exam. A final oral exam can also be scheduled, with duration up to 60 minutes. The final grade is formed based on the points from the partial written exams, the tests, the homework and the project assignments and the final oral exam (if scheduled).
2. In the planned exam sessions, a final written exam is taken (lasting 120 minutes). For students who have passed the final written exam, a final oral exam can also be scheduled (with duration up to 60 minutes). The final grade is formed based on the points from the final written exam, the tests, the homework and the project assignments and the final oral exam (if scheduled)
21.    Language Macedonian and English
22.    Method of monitoring of teaching quality Internal evaluation and surveys.
23    Literature
23.1. Required Literature
No. Author Title Publisher Year
1 J-S. R. Jang, C-T. Sun, and E. Mizutani Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence Prentice Hall 1997
2 Kevin Passino and Steve Yurkovich Fuzzy Control 1997
3 Kevin Gurney An Introduction to Neural Networks CRC Press 1997
23.2. Additional Literature
No. Author Title Publisher Year
1 Stamatios V. Kartalopoulos  Understanding Neural Networks and Fuzzy Logic: Basic Concepts and Applications  Wiley-IEEE Press  1995
2  R. A. Aliev and R. R. Aliev  Soft Computing & Its Applications  World Scientific Publishing Company  2001
3  Clive L. Dym and Raymond E. Levitt

Knowledge-Based Systems in Engineering

  McGraw-Hill  1991