Course detail

Intelligent Systems

FIT-ISDAcad. year: 2019/2020

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.

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.

Prerequisites

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

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

Kecman, V.: Learning and Soft Computing, The MIT Press, 2001, ISBN 0-262-11255-8
Negnevitsky M.: Artificial Intelligence - A Guide to Intelligent systems, Pearson Education Limited 2002, ISBN 0201-71159-1
Zaknih, A.: Neural Networks for Intelligent Signal Processing, World Scientific Publishing Co. Pte. Ltd., 2003, ISBN 981-238-305-0
Rutkowski, L.: Flexible Neuro-Fuzzy Systems, Kluwer Academic Publishers, 2004, ISBN: 1-4020-8042-5
Liu, P., Li, H.: Fuzzy Neural Network Theory and Application, World Scientific Publishing Co. Pte. Ltd., 2004, ISBN 981-538-786-2
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
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

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

Not applicable.

Language of instruction

Czech, English

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.

Classification of course in study plans

  • Programme VTI-DR-4 Doctoral

    branch DVI4 , any year of study, summer semester, 0 credits, optional

  • Programme VTI-DR-4 Doctoral

    branch DVI4 , any year of study, summer semester, 0 credits, optional

  • Programme VTI-DR-4 Doctoral

    branch DVI4 , any year of study, summer semester, 0 credits, optional

  • Programme VTI-DR-4 Doctoral

    branch DVI4 , any year of study, summer semester, 0 credits, optional

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

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

Projects

26 hours, compulsory

Teacher / Lecturer

Syllabus


  • Individual projects - designs of intelligent systems for solving some practical problem

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