Course: Theory and Applications of Artificial Neural Networks

« Back
Course title Theory and Applications of Artificial Neural Networks
Course code UI/DI004
Organizational form of instruction no contact
Level of course Doctoral
Year of study not specified
Semester Winter and summer
Number of ECTS credits 0
Language of instruction Czech
Status of course Compulsory
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Course availability The course is available to visiting students
Lecturer(s)
  • MARČEK Dušan, prof. Ing. CSc.
Course content
1. The structure of biological neuron, mathematical model of a simple neuron and a multi-layer neural network. Features and applications of artificial neural networks. 2. Active, adaptive and organization dynamics, neural training schemes (supervised/unsupervised/reinforcement). Training and testing sets, training process, the overfitting problem. 3. The perceptron and its training algorithm. Implementation of simple logic functions. Limited capabilities of single-layer perceptron. 4. Multilayer networks and the Backpropagation (BP) algorithm. Modifications and improvements of the BP algorithm (training speed adjustment, the momentum term, gain adaptation). 5. Efficient methods for training of the multilayer perceptron: conjugate-gradient methods, resilient propagation, further methods. 6. Hetero- and auto-associative networks, topology and training, synchronous and asynchronous models. The Hopfield model, stability and energy, storage capacity 7. Radial Basis Function networks, organization and active dynamics. Three phases of training, properties, applications, a comparison with multilayer perceptron. 8. Competitive networks, the vector quantization problem, Lloyd's algorithm. The Kohonen training rule, the UCL, DCL a SCL variants of training. 9. Self-organizing maps - SOM, description and applications, the neighbourhood function, examples of single- and two-dimensional maps. 10. The ART networks, principles and properties, the vigilance function.

Learning activities and teaching methods
Interactive lecture, Lecture with a video analysis
Recommended literature
  • Hassoun, M.H. Fundamentals ofArtificial Neural Networks.. The MIT Press, Cambridge, Messachusetts,London, 1994.
  • Haykin, S. Kalman Filtering and Neural Networks. NY: John Wiley and Sons, 2002.
  • Hertz, J.; Krogh, A.; Palmer, R., G. Introduction to the Theory of Neural Computation.. Addison-esley, 1991.
  • Kecman, V. Learning and Soft Computing, Support Vector Machines, Neural Networks, and Fuzzy Logic Models.. Massachusetts Institute of Technology, The MIT Press, 2001.


Study plans that include the course
Faculty Study plan (Version) Branch of study Category Recommended year of study Recommended semester
Faculty of Philosophy and Science in Opava Autonomous Systems (1) Informatics courses - -
Faculty of Philosophy and Science in Opava Autonomous Systems (1) Informatics courses - -
Faculty of Philosophy and Science in Opava Autonomous Systems (1) Informatics courses - -
Faculty of Philosophy and Science in Opava Autonomous Systems (1) Informatics courses - -