Course details

Soft Computing

SFC Acad. year 2017/2018 Winter semester 5 credits

Current academic year

Soft computing covers non-traditional technologies or approaches to 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, ACO (Ant Colony Optimization) and PSO (Particle Swarm Optimization) 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

Credit+Examination (written)

Time span

Assessment points

  • 55 pts final exam (written part)
  • 15 pts mid-term test (written part)
  • 30 pts projects

Department

Department of Intelligent Systems (UITS)

Subject specific learning outcomes and competences

  • Students will acquaint with basic types of neural networks and with their applications.
  • Students will acquaint with fundamentals of theory of fuzzy sets and fuzzy logic including design of fuzzy controller.
  • Students will learn to solve optimization problems using Genetic, Ant Colony Optimization and Particle Swarm Optimization algorithms.
  • Students will acquaint with fundamentals of probability reasoning theory.
  • Students will acquaint with fundamentals of rouhg sets theory and with use of these sets for data mining.
  • Students will acquaint with fundamentals of chaos theory.

  • Students will learn terminology in Soft-computing field both in Czech and in English languages.
  • 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.

Prerequisite knowledge and skills

  • Programming in C++ or Java languages.
  • Basic knowledge of differential calculus and probability theory.

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

Syllabus of seminars

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

Syllabus - others, projects and individual work of students:
Individual project - solving real-world problem (classification, optimization, association, controlling).

Progress assessment

At least 20 points earned during semester (mid-term test and project).

Controlled instruction

  • Mid-term written examination - 15 points.
  • Project - 30 points.
  • Final written examination - 55 points; The minimal number of points which can be obtained from the final written examination is 25. Otherwise, no points will be assigned to a student.

Course inclusion in study plans

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