Course detail

Intelligent Systems

FIT-ISDAcad. year: 2017/2018

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.

Language of instruction

Czech

Number of ECTS credits

0

Mode of study

Not applicable.

Guarantor

doc. Ing. František Zbořil, CSc.

Department

Department of Intelligent Systems (UITS)

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.

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

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

Course curriculum

Syllabus of lectures:
ol> 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:
  • Individual projects - designs of intelligent systems for solving some practical problem

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.

Specification of controlled education, way of implementation and compensation for absences

There are no checked study.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic 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

Recommended reading

  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

Classification of course in study plans

  • Programme VTI-DR-4 Doctoral

    branch DVI4 , any year of study, summer semester, elective

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

doc. Ing. František Zbořil, CSc.

Syllabus

ol>
  • 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

26 hours, optionally

Teacher / Lecturer

doc. Ing. František Zbořil, CSc.