Course details

Soft Computing

SFC Acad. year 2006/2007 Winter semester 5 credits

Current academic year

Soft computing covers non-traditional technologies or approaches for solving hard real-world problems. Content of course, in accordance with meaning of its name, is as follow: Tolerance of imprecision and uncertainty as the main attributes of soft computing theories. Neural networks. Fuzzy logic. Genetic algorithms. Probabilistic reasoning. Rough sets. Chaos.  Hybrid approaches (combinations of neural networks, fuzzy logic and genetic algorithms).

Guarantor

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

Language of instruction

Czech

Completion

Examination

Time span

  • 26 hrs lectures
  • 26 hrs projects

Department

Department of Intelligent Systems (UITS)

Subject specific learning outcomes and competences

Students acquire knowledge of soft computing theories fundamentals and so they will be able to design program systems using approaches of these theories for solving various real-world problems.

Students awake the importance of tolerance of imprecision and uncertainty for design of robust and low-cost intelligent machines.

Learning objectives

To give students knowledge of soft computing theories fundamentals, i.e. of fundamentals of non-traditional technologies and approaches to solving hard real-world problems, namely of fundamentals of artificial neural networks, fuzzy sets and fuzzy logic and genetic algorithms.

Prerequisite knowledge and skills

There are no prerequisites

Study literature

    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

Fundamental literature

  • Rutkowski, L.: Flexible Neuro-Fuzzy Systems, Kluwer Academic Publishers, 2004, ISBN 1-4020-8042-5

Syllabus of lectures

  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, SDM and Neocognitron neural networks
  6. Neural networks as associative memories (Hopfield, BAM).
  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).

Progress assessment

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

Controlled instruction

  1. Mid-term written test
  2. Individual project
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