Course detail
Intelligent Systems
FIT-ISDAcad. year: 2021/2022
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)
- Fuzzy expert systems
- Knowledge engineering using soft-computing
- Intelligent sensor systems
- Neural networks in intelligent systems
- Fuzzy control systems
- Neuro-fuzzy control systems
- Rough sets in intelligent systems
- Genetic algorithms in intelligent systems
- Inteligent robots
- Navigation of mobile robots
Language of instruction
Mode of study
Guarantor
Department
Learning outcomes of the course unit
A detailed overview of the current state of intelligent systems and the ability to use the acquired knowledge in their own research.
Prerequisites
Co-requisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Course curriculum
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
Basic literature
Recommended reading
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 DIT Doctoral 0 year of study, summer semester, compulsory-optional
- Programme DIT Doctoral 0 year of study, summer semester, compulsory-optional
- 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 DIT-EN Doctoral 0 year of study, summer semester, compulsory-optional
- Programme DIT-EN Doctoral 0 year of study, summer semester, compulsory-optional
- 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
Teacher / Lecturer
Syllabus
- Introduction, soft computing and ISY
- Expert systems
- Intelligent information systems
- Machine translation systems
- Surrounding environment perception, intelligent sensor systems
- Analysis of sensor data, environment model design
- Planning of given tasks accomplishments
- Control systems with neural networks
- Fuzzy control systems
- Neuro-fuzzy systems
- Utilization of rough sets and genetic algorithms in ISY
- Intelligent robotic systems
- Navigation of mobile robots
Project
Teacher / Lecturer
Syllabus
- Two individual projects - designs of intelligent systems for solving some practical problems.
Guided consultation in combined form of studies
Teacher / Lecturer