Course details
Neural Networks
NEU Acad. year 2003/2004 Winter semester 6 credits
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.
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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
- Programme EI-BC-3, field VTB, 2nd year of study, Elective
- Programme EI-BC-3 (in English), field VTB, 2nd year of study, Elective
- Programme EI-MGR-3, field VTN, 3rd year of study, Elective
- Programme EI-MGR-5, field VTI, 3rd year of study, Elective
- Programme EI-MGR-5 (in English), field VTI, 3rd year of study, Elective