Course details

Neural Networks

NEU Acad. year 2003/2004 Winter semester 6 credits

Current academic year

Artificial neuron, basis and activation functions. Classification of neural networks. Principles of individual neural networks (topology, learning, responses, typical applications): "Adaline, Perceptron, Madaline, BPN, adaptive feedforward multilayer networks, self-organizing neural networks, CPN, LVQ, RBF, RCE, Hopfield neural networks, BAM, SDM, Boltzmann machine, Neocognitron". Genetic algorithm, fuzzy systems, rough sets and neural networks.

Details ...

Guarantor

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

Language of instruction

Czech

Completion

Examination

Time span

Department

Faculty of Information Technology (FIT)

Subject specific learning outcomes and competences

Students acquire knowledge of particular types of neural networks and so they will be able to design programs using these networks to solving of various practical problems.

Learning objectives

To give the students the knowledge of fundamentals of neural network theory and the knowledge of topologies, learning, responses and possible practical applications of various types of these networks.

Study literature

  • Šíma,J., Neruda,R.: Teoretické otázky neuronových sítí, MATFYZPRESS, 1996, ISBN 80-85863-18-9
  • Novák,M. a kol.: Umělé neuronové sítě, C.H. Beck, 1998, ISBN 80-7179-132-6

Fundamental literature

  • Mehrotra,K., Mohan,C.K., Ranka S: Artificial Neural Networks, The MIT Press, 1997, ISBN 0-262-13328-8
  • Hassoun, M.H.: Artificial Neural Networks, The MIT Press, 1995, ISBN 0-262-08239-X
  • Haykin,S.: Neural Networks, Macmillan College Publishing Company, Inc., 1994, ISBN 0-02-352761-7

Course inclusion in study plans

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