Course detail

Soft Computing

FIT-SFCAcad. year: 2010/2011

Not applicable.

Language of instruction

Czech

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

doc. Ing. František Zbořil, CSc.

Department

Department of Intelligent Systems (UITS)

Learning outcomes of the course unit

Not applicable.

Prerequisites

Not applicable.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

Not applicable.

Course curriculum

Not applicable.

Work placements

Not applicable.

Aims

Not applicable.

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

Not applicable.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Kriesel, D.: A Brief Introduction to Neural Networks, 2005, Fundamentals of the New Artificial Intelligence, Springer-Verlag New York, Inc., 2008. ISBN 978-1-84628-838-8 
Rutkowski, L.: Flexible Neuro-Fuzzy Systems, Kluwer Academic Publishers, 2004, ISBN 1-4020-8042-5
Russel,S., Norvig,P.: Artificial Intelligence, Prentice-Hall, Inc., third edition 2010, ISBN 0-13-604259-7

Recommended reading

  1. Mehrotra, K., Mohan, C. K., Ranka, S.: Elements of Artificial Neural Networks, The MIT Press, 1997, ISBN 0-262-13328-8
  2. Munakata, T.: Fundamentals of the New Artificial Intelligence, Springer-Verlag New York, Inc., 2008. ISBN 978-1-84628-838-8
  3. Russel, S., Norvig, P.: Artificial Intelligence, Prentice-Hall, Inc., 1995, ISBN 0-13-360124-2, second edition 2003, ISBN 0-13-080302-2, third edition 2010, ISBN 0-13-604259-7

Classification of course in study plans

  • Programme IT-MGR-2 Master's

    branch MGM , any year of study, winter semester, elective
    branch MSK , any year of study, winter semester, elective
    branch MPS , any year of study, winter semester, elective
    branch MIS , any year of study, winter semester, elective
    branch MBS , any year of study, winter semester, elective
    branch MMI , any year of study, winter semester, elective
    branch MMM , any year of study, winter semester, compulsory-optional
    branch MIN , 1. year of study, winter semester, compulsory
    branch MBI , 2. year of study, winter semester, compulsory
    branch MPV , 2. year of study, winter semester, compulsory-optional

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

doc. Ing. František Zbořil, CSc.

Syllabus

  1. Introduction, Soft Computing concept explanation. Importance of tolerance of imprecision and uncertainty.
  2. Biological and artificial neuron, neural networks. Adaline, Perceptron. Madaline and BP (Back Propagation) neural networks.
  3. Adaptive feedforward multilayer networks.
  4. RBF and RCE neural networks. Topologic organized neural networks, competitive learning, Kohonen maps.
  5. CPN , LVQ, ART, Neocognitron neural networks
  6. Neural networks as associative memories (Hopfield, BAM, SDM).
  7. Solving optimization problems using neural networks. Stochastic neural networks, Boltzmann machine.
  8. Fuzzy sets, fuzzy logic and fuzzy inference.
  9. Genetic algorithms.
  10. Probabilistic reasoning.
  11. Rough sets.
  12. Chaos.
  13. Hybrid approaches (neural networks, fuzzy logic, genetic algorithms sets).

Project

26 hours, optionally

Teacher / Lecturer

doc. Ing. František Zbořil, CSc.