Course details

Intelligent Systems

ISD Acad. year 2016/2017 Summer semester

Current academic year

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.

Guarantor

Zbořil František, doc. Ing., CSc. (DITS)

Language of instruction

Czech, English

Completion

Examination

Time span

  • 26 hrs lectures
  • 26 hrs projects

Assessment points

  • 60 pts final exam (written part)
  • 40 pts projects

Department

Department of Intelligent Systems (UITS)

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.

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.

Prerequisite knowledge and skills

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

Study literature

    1. Rutkowski, L.: Flexible Neuro-Fuzzy Systems, Kluwer Academic Publishers, 2004, ISBN: 1-4020-8042-5
    2. Mitchell, H. B.: Multi-Sensor Data Fusion, Springer-Verlag Berlin Heidelberg 2007, ISBN 978-3-540-71463-7
    3. Munakata,T.: Fundamentals of the New Artificial Intelligence, Springer, 2008, ISBN 978-1-84628-838-8 
    4. Shi, Z.: Advanced Artificial Intelligence, World Scientific Publishing Co. Pte. Ltd., 2011, ISBN-13 978-981-4291-34-7
    5. Iba, H., Noman, N.: New Frontier in Evolutionary Algorithms, Imperial College Press, 2012, ISBN-13 978-1-84816-681-3

Fundamental literature

    1. Kecman, V.: Learning and Soft Computing, The MIT Press, 2001, ISBN 0-262-11255-8
    2. Negnevitsky M.: Artificial Intelligence - A Guide to Intelligent systems, Pearson Education Limited 2002, ISBN 0201-71159-1
    3. Zaknih, A.: Neural Networks for Intelligent Signal Processing, World Scientific Publishing Co. Pte. Ltd., 2003, ISBN 981-238-305-0
    4. Rutkowski, L.: Flexible Neuro-Fuzzy Systems, Kluwer Academic Publishers, 2004, ISBN: 1-4020-8042-5
    5. Liu, P., Li, H.: Fuzzy Neural Network Theory and Application, World Scientific Publishing Co. Pte. Ltd., 2004, ISBN 981-538-786-2
    6. Mitchell, H. B.: Multi-Sensor Data Fusion, Springer-Verlag Berlin Heidelberg 2007, ISBN 978-3-540-71463-7
    7. Munakata,T.: Fundamentals of the New Artificial Intelligence, Springer, 2008, ISBN 978-1-84628-838-8
    8. Shi, Z.: Advanced Artificial Intelligence, World Scientific Publishing Co. Pte. Ltd., 2011, ISBN-13 978-981-4291-34-7
    9. Iba, H., Noman, N.: New Frontier in Evolutionary Algorithms, Imperial College Press, 2012, ISBN-13 978-1-84816-681-3

Syllabus of lectures

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  • 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

Progress assessment

Study evaluation is based on marks obtained for specified items. Minimimum number of marks to pass is 50.

Controlled instruction

There are no checked study.

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