Course details

Intelligent Systems

ISD Acad. year 2024/2025 Summer semester

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

Course coordinator

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

Lecturer

Instructor

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.

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.

Course inclusion in study plans

  • Programme DIT, any year of study, Compulsory-Elective group O
  • Programme DIT, any year of study, Compulsory-Elective group O
  • Programme DIT-EN (in English), any year of study, Compulsory-Elective group O
  • Programme DIT-EN (in English), any year of study, Compulsory-Elective group O
  • Programme VTI-DR-4, field DVI4, any year of study, Elective
  • Programme VTI-DR-4, field DVI4, any year of study, Elective
  • Programme VTI-DR-4 (in English), field DVI4, any year of study, Elective
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