Course details

Intelligent Systems

ISD Acad. year 2020/2021 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

Deputy Guarantor

Language of instruction

Czech

Completion

Examination

Time span

26 hrs lectures, 26 hrs projects

Assessment points

60 exam, 40 projects

Department

Lecturer

Course Web Pages

Subject specific learning outcomes and competences

Students acquire knowledge of principles of intelligent systems and so they will be able to design these systems for solving of various practical problems.

Generic learning outcomes and competences

A detailed overview of the current state of intelligent systems and the ability to use the acquired knowledge in their own research.

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.

Why is the course taught

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

Prerequisite kwnowledge and skills

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

Corequisite knowledge and skills

None.

Study literature

  1. Shi, Z.: Advanced Artificial Intelligence, World Scientific Publishing Co. Pte. Ltd., 2011, ISBN-13 978-981-4291-34-7
  2. Iba, H., Noman, N.: New Frontier in Evolutionary Algorithms, Imperial College Press, 2012, ISBN-13 978-1-84816-681-3
  3. Bramer, M.: Principles of Data Mining, Second edition, Springer-Verlag London 2013, ISBN 978-1-4471-4883-8
  4. Fraden, J.: Handbook of Modern Sensors, Springer  Springer International Publishing, 2016, ISBN 978-3-319-19302-1
  5. 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
  6. Lynch, K. M., Park, F,C,: Modern Robotics. Mechanics, Planning, and Control, Cambridge U. Press, 2017, ISBN: 9781107156302

Fundamental literature

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

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 of numerical exercises

The course does not have numerical exercises.

Syllabus of laboratory exercises

The course does not have laboratory exercises.

Syllabus of computer exercises

The course does not have computer exercises.

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.

Controlled instruction

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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