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Last update: Procházka Aleš prof. Ing. CSc. (25.07.2013)
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Last update: Procházka Aleš prof. Ing. CSc. (25.07.2013)
Students will know how
(i) to model multilayer and reccurent neural networks, (ii) to create pattern matrices forming inputs for neural networks, (iii) to optimize neural networks for data classification, (iv) to use neural networks for noise cancelling and signal prediction |
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Last update: TAJ445 (30.09.2013)
R: S. Haykin: Neural Networks, Prentice Hall, 1999, ISBN 0132733501 |
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Last update: Procházka Aleš prof. Ing. CSc. (25.07.2013)
http://uprt.vscht.cz/prochazka/pedag/lectures/SP0_MATLAB_2006EN.pdf |
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Last update: Procházka Aleš prof. Ing. CSc. (08.12.2005)
1. Fundamentals of MATLAB environment, basic operations, data files processing 2. Visualization tools in MATLAB, symbolic mathematics, principles of SIMULINK 3. Basic mathematical models of neurons, transfer functions, threshold, error surface evaluation 4. Single-layer networks, perceptron learning rule, application to classification problems 5. Adaptive linear networks, Widrow-Hoff learning rule, training and coefficients optimization 6. Neural networks in adaptive noise cancellation 7. Multi-layer networks, error function, optimization of parameters, gradient descent method 8. Basic optimization methods (backpropagation, Levenberg-Marquardt algorithm) 9. Neural networks in signal prediction and system modeling, learning and generalization 10. Radial basis networks, transfer functions definition, network structure optimization 11. Associative learning rules, self-organizing networks and maps, Hebian learning, Kohonen rule 12. Neural networks in segmentation, feature extraction and classification, competitive learning 13. Simulation of artificial neural networks in the SIMULINK environment 14. Neural networks in system identification and control, supervised and unsupervised learning |
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Last update: Procházka Aleš prof. Ing. CSc. (25.07.2013)
Knowledge of MATLAB and digital signal and image processing methods |
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Last update: Procházka Aleš prof. Ing. CSc. (25.07.2013)
Digital Signal and Image Processing |
Teaching methods | ||||
Activity | Credits | Hours | ||
Konzultace s vyučujícími | 0.5 | 14 | ||
Účast na přednáškách | 1 | 28 | ||
Příprava na přednášky, semináře, laboratoře, exkurzi nebo praxi | 0.5 | 14 | ||
Práce na individuálním projektu | 1 | 28 | ||
Příprava na zkoušku a její absolvování | 1 | 28 | ||
Účast na seminářích | 1 | 28 | ||
5 / 5 | 140 / 140 |
Coursework assessment | |
Form | Significance |
Regular attendance | 30 |
Report from individual projects | 30 |
Oral examination | 40 |