Course details
Intelligent Systems
ISD Acad. year 2021/2022 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)
- 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
Guarantor
Course coordinator
Language of instruction
Completion
Time span
- 26 hrs lectures
- 26 hrs projects
Assessment points
- 60 pts final exam
- 40 pts projects
Department
Lecturer
Instructor
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.
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 knowledge 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
- Munakata,T.: Fundamentals of the New Artificial Intelligence, Springer, 2008, ISBN 978-1-84628-838-8
- Shi, Z.: Advanced Artificial Intelligence, World Scientific Publishing Co. Pte. Ltd., 2011, ISBN-13 978-981-4291-34-7
- 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
- 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
- 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
- Fraden, J.: Handbook of Modern Sensors, Springer Springer International Publishing, 2016, ISBN 978-3-319-19302-1
- Lynch, K. M., Park, F,C,: Modern Robotics. Mechanics, Planning, and Control, Cambridge U. Press, 2017, ISBN: 9781107156302
Syllabus of lectures
- 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
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
- 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
- Programme VTI-DR-4 (in English), field DVI4, any year of study, Elective