UIN3042 Artificial Neural Networks

Faculty of Philosophy and Science in Opava
Winter 2011
Extent and Intensity
2/2/0. 6 credit(s). Type of Completion: zk (examination).
Teacher(s)
prof. Ing. Dušan Marček, CSc. (lecturer)
doc. Ing. Petr Sosík, Dr. (lecturer)
Guaranteed by
doc. Ing. Petr Sosík, Dr.
Institute of Computer Science – Faculty of Philosophy and Science in Opava
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
This classical branch of Artificial Intelligence makes use of mathematical aspects of behavior of neural cells in living organisms. The result is a sequence of "neural" algorithms capable to learn from examples, to generalize knowledge and to search for approximate solutions of intractable problems. These algorithms can be run on special parallel machines but also on classical computers.
Syllabus
  • 1. The structure of biological neuron, the way and time patterns of transfer of information. Mathematical model of simple neuron and multi-layer neural network.
    2. Neural learning schemes. Topology and structure of neural networks.
    3. McCuloch-Pitts neurons. Neural networks without feedback: neural models of simple logical functions, learning principles. Training algorithms, the perceptron algorithm.
    4. Weight adaptation in networks with one or more hidden layers: the Backpropagation (BP) algorithm. Modifications and improvements of the BP algorithm. Application examples.
    5. Associative memories, hetero- and auto-associative networks, synchronous and asynchronous models. Information storage and recall. Learning algorithms, adaptive resonance dynamics. The Lyapunov function and energy.
    6. Hopfield model, proof of the principle of minimal energy. Applications: NP-complete problems, data/image reconstruction. Memory capacity of Hopfield networks.
    7. Unsupervised learning, Hebb and Oja learnign rule. Networks for extraction of principal components, Sanger rule.
    8. Competitive network architecture, Kohonen learnig rule. Clustering, self-organization, variants of learning rules. Self-organizing maps - SOM. Learnign Vector Quantization (LVQ), learning rules for adaptive vector quantization (AVQ).
    9. Fuzzy neural networks. Fuzzy neural architecture based on fuzzy arithmetics. Fuzzy neural architecture based on fuzzy logics. Learning schemes for fuzzy neural nets.
Literature
    recommended literature
  • MARČEK, D. Neuronové sítě a fuzzy časové řady. Opava: SU Opava, 2002. ISBN 80-7248-157-6. info
  • NERUDA, R., ŠÍMA, J. Teoretické otázky neuronových sítí. Matfyzpress, Praha, 1996. info
  • SACKS, O. Muž, který si pletl manželku s kloboukem. Praha: Mladá Fronta, 1993. info
  • NOVÁK, M., FABER, J., KUFUDAKI, O. Neuronové sítě a informační systémy živých organismů. Grada, Praha, 1993. info
  • HERTZ, J. et. al. Introduction to the Theory of Neural Computation. Addison-Wesley, New York, 1991. info
Assessment methods
Oral exam
Language of instruction
Czech
Further Comments
The course can also be completed outside the examination period.
The course is also listed under the following terms Winter 1993, Winter 1994, Winter 1995, Winter 1996, Winter 1997, Winter 1998, Winter 1999, Winter 2000, Winter 2001, Winter 2002, Winter 2003, Winter 2004, Summer 2006, Winter 2006, Winter 2007, Winter 2008, Winter 2009, Winter 2010, Winter 2012, Winter 2013, Winter 2014, Winter 2015, Winter 2016, Winter 2017, Winter 2018, Winter 2019, Winter 2020, Winter 2021, Winter 2022, Winter 2023.
  • Enrolment Statistics (Winter 2011, recent)
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