Course detail

Soft Computing

FIT-SFCAcad. year: 2017/2018

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).

Language of instruction

Czech

Number of ECTS credits

Mode of study

Not applicable.

Guarantor

doc. Ing. František Zbořil, CSc.

Department

Department of Intelligent Systems (UITS)

Learning outcomes of the course unit

  • 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.

Prerequisites

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

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

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

Course curriculum

    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).

Work placements

Not applicable.

Aims

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.

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

  • 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.

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 
Russel,S., Norvig,P.: Artificial Intelligence, Prentice-Hall, Inc., third edition 2010, ISBN 0-13-604259-7
Rutkowski, L.: Flexible Neuro-Fuzzy Systems, Kluwer Academic Publishers, 2004, ISBN 1-4020-8042-5

Recommended reading

Mehrotra, K., Mohan, C. K., Ranka, S.: Elements of Artificial Neural Networks, The MIT Press, 1997, ISBN 0-262-13328-8 Munakata, T.: Fundamentals of the New Artificial Intelligence, Springer-Verlag New York, Inc., 2008. ISBN 978-1-84628-838-8 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-MSC-2 Master's

    branch MMI , 0 year of study, winter semester, elective
    branch MBI , 2 year of study, winter semester, compulsory
    branch MSK , 0 year of study, winter semester, elective
    branch MMM , 0 year of study, winter semester, compulsory-optional
    branch MBS , 0 year of study, winter semester, elective
    branch MIS , 0 year of study, winter semester, elective
    branch MIN , 1 year of study, winter semester, compulsory
    branch MGM , 0 year of study, winter semester, elective
    branch MPV , 0 year of study, winter semester, compulsory-optional

E-learning texts

SFC 2020
Soft Computing 2020.pdf 7.1 MB