Title:

Intelligent Systems

Code:ISD
Ac.Year:2018/2019
Term:Summer
Curriculums:
ProgrammeFieldYearDuty
CSE-PHD-4DVI4-Elective
Language of Instruction:Czech
Private info:http://www.fit.vutbr.cz/study/courses/ISD/private/
Completion:examination
Type of
instruction:
Hour/semLecturesSeminar
Exercises
Laboratory
Exercises
Computer
Exercises
Other
Hours:2600026
 ExamsTestsExercisesLaboratoriesOther
Points:6000040
Guarantor:Zbořil František V., doc. Ing., CSc. (DITS)
Faculty:Faculty of Information Technology BUT
Department:Department of Intelligent Systems FIT BUT
 
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.
Description:
  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.
Knowledge and skills required for the course:
  Basic knowledge of artificial intelligence in a scope of Fundamentals of Artificial Intelligence course of current study program in FIT. 
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.
Syllabus of lectures:
 
  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
Syllabus - others, projects and individual work of students:
 
  • Individual projects - designs of intelligent systems for solving some practical problem
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
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
 

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