Course detail

Intelligent Systems

FIT-ISDAcad. year: 2020/2021

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

Language of instruction

Czech

Mode of study

Not applicable.

Guarantor

doc. Ing. František Zbořil, CSc.

Department

Department of Intelligent Systems (UITS)

Learning outcomes of the course unit

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.

Prerequisites

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

Co-requisites

None.

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

Group consultations once every two weeks.
Exam prerequisites:
The course has no credit.

Course curriculum

Not applicable.

Work placements

Not applicable.

Aims

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.

Specification of controlled education, way of implementation and compensation for absences

Defenses of projects, oral final exam. Replacement of missed defense of the project in agreement with the subject guarantor.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Not applicable.

Recommended reading

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
Bramer, M.: Principles of Data Mining, Second edition, Springer-Verlag London 2013, ISBN 978-1-4471-4883-8
Fraden, J.: Handbook of Modern Sensors, Springer  Springer International Publishing, 2016, ISBN 978-3-319-19302-1
Iba, H., Noman, N.: New Frontier in Evolutionary Algorithms, Imperial College Press, 2012, ISBN-13 978-1-84816-681-3
Kruse, R., Borgelt, Ch., Braune, Ch., Mostaghim, S., Steinbrecher, M.:Computational Intelligence - A Methodological Introduction, Second Edition Springer-Verlag London, 2016, ISBN 978-1-4471-7294-9
Lynch, K. M., Park, F,C,: Modern Robotics. Mechanics, Planning, and Control, Cambridge U. Press, 2017, ISBN: 9781107156302
Mitchell, H. B.: Multi-Sensor Data Fusion, Springer-Verlag Berlin Heidelberg 2007, ISBN 978-3-540-71463-7
Munakata,T.: Fundamentals of the New Artificial Intelligence, Springer, 2008, ISBN 978-1-84628-838-8
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
Shi, Z.: Advanced Artificial Intelligence, World Scientific Publishing Co. Pte. Ltd., 2011, ISBN-13 978-981-4291-34-7

Classification of course in study plans

  • Programme CSE-PHD-4 Doctoral

    branch DVI4 , 0 year of study, summer semester, elective

  • Programme CSE-PHD-4 Doctoral

    branch DVI4 , 0 year of study, summer semester, elective

  • Programme CSE-PHD-4 Doctoral

    branch DVI4 , 0 year of study, summer semester, elective

  • Programme CSE-PHD-4 Doctoral

    branch DVI4 , 0 year of study, summer semester, elective

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

doc. Ing. František Zbořil, CSc.

Syllabus

  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

Project

26 hod., compulsory

Teacher / Lecturer

doc. Ing. František Zbořil, CSc.

Syllabus

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

Guided consultation in combined form of studies

26 hod., optionally

Teacher / Lecturer

doc. Ing. František Zbořil, CSc.