Course details

Intelligent Systems

ISD Acad. year 2023/2024 Summer semester

Current academic year

Tolerance of imprecision and uncertainty as main attribute of ISY. Intelligent systems based on combinations of several theories - neural networks, fuzzy sets, rough sets and genetic algorithms: expert systems, intelligent information systems, machine translation systems, intelligent sensor systems, intelligent control systems, intelligent robotic systems.

Topics for the SDE (state doctoral exam)

  1. Fuzzy expert systems
  2. Knowledge engineering using soft-computing
  3. Intelligent sensor systems
  4. Neural networks in intelligent systems
  5. Fuzzy control systems
  6. Neuro-fuzzy control systems
  7. Rough sets in intelligent systems
  8. Genetic algorithms in intelligent systems
  9. Inteligent robots
  10. Navigation of mobile robots

Guarantor

Janoušek Vladimír, doc. Ing., Ph.D. (DITS)

Course coordinator

Zbořil František, doc. Ing., Ph.D. (DITS)

Language of instruction

Czech, English

Completion

Examination

Time span

  • 26 hrs lectures
  • 26 hrs projects

Assessment points

  • 60 pts final exam
  • 40 pts projects

Department

Department of Intelligent Systems (UITS)

Lecturer

Janoušek Vladimír, doc. Ing., Ph.D. (DITS)

Instructor

Janoušek Vladimír, doc. Ing., Ph.D. (DITS)

Course Web Pages

Public pages of the course

Learning objectives

To give the students the knowledge of intelligent systems design (control, production, etc.) based on combinations of theories of neural networks, fuzzy sets, rough sets and genetic algorithms.
Students acquire knowledge of principles of intelligent systems and so they will be able to design these systems for solving of various practical problems.
A detailed overview of the current state of intelligent systems and the ability to use the acquired knowledge in their own research.

Why is the course taught

Intelligent systems are becoming an integral and important part of everyday life.

Prerequisite knowledge and skills

Basic knowledge of artificial intelligence in a scope of Fundamentals of Artificial Intelligence course of current study program at FIT. 

Study literature

  • Munakata,T.: Fundamentals of the New Artificial Intelligence, Springer, 2008, ISBN 978-1-84628-838-8
  • Iba, H., Noman, N.: New Frontier in Evolutionary Algorithms, Imperial College Press, 2012, ISBN-13 978-1-84816-681-3
  • Mitchell, H. B.: Multi-Sensor Data Fusion, Springer-Verlag Berlin Heidelberg 2007, ISBN 978-3-540-71463-7
  • Bramer, M.: Principles of Data Mining, Second edition, Springer-Verlag London 2013, ISBN 978-1-4471-4883-8
  • Raza, M. S., Qamar, U.: Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications, Springer Nature, 2017, ISBN 978-981-10-4964-4
  • Bianchi, F. M., Maiorino, E., Kampffmeyer, M. C., Rizzi, A., Jenssen, R.:Recurrent Neural Networks for Short-Term Load Forecasting - An Overview and Comparative Analysis, SpringerBriefs in Computer Science, 2017, ISBN 978-3-319-70337-4

Syllabus of lectures

  1. Introduction, soft computing and ISY
  2. Expert systems
  3. Intelligent information systems
  4. Machine translation systems
  5. Surrounding environment perception, intelligent sensor systems
  6. Analysis of sensor data, environment model design
  7. Planning of given tasks accomplishments
  8. Control systems with neural networks
  9. Fuzzy control systems
  10. Neuro-fuzzy systems
  11. Utilization of rough sets and genetic algorithms in ISY
  12. Intelligent robotic systems
  13. Navigation of mobile robots

Syllabus - others, projects and individual work of students

  • Two individual projects - designs of intelligent systems for solving some practical problems.

Progress assessment

Group consultations once every two weeks.
Defenses of projects, oral final exam. Replacement of missed defense of the project in agreement with the subject guarantor.

Exam prerequisites

The course has no credit.

Course inclusion in study plans

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