Course details
Neural Networks, Adaptive and Optimum Filtering
Guarantor
Jan Jiří, prof. Ing., CSc. (UBMI)
Language of instruction
Czech, English
Completion
Examination
Time span
- 39 hrs lectures
Department
Study literature
- J.Jan: Digital Signal Filtering, Analysis and Restoration. IEE Publishing, London, UK, 2000
- B. Kosko (ed.): Neural Networks for signal processing. Prentice Hall 1992
- Jan, J,: Číslicová filtrace, analýza a restaurace signálů. 2. rozš. vydání. VUTIUM Brno 2003
Fundamental literature
- B. Kosko: Neural Networks and fuzzy systems. Prentice Hall 1992
- B. Kosko (ed.): Neural Networks for signal processing. Prentice Hall 1992
- S. Haykin: Neural Networks. Prentice Hall 1994
- J.G.Proakis, et al.: Advanced digital signal processing. McMillan Publ. 1992
- J.Jan: Digital Signal Filtering, Analysis and Restoration. IEE Publishing, London, UK, 2000
- P.M.Clarkson: Optimal and Adaptive Signal Processing. CRC Press, 1993
- S. Haykin: Adaptive Filter Theory. Prentice-Hall Int. 1991
- V.K.Madisetti, D.B.Williams (eds.): The Digital Signal Processing Handbook. CRC Press & IEEE Press, 1998
Syllabus of lectures
- Architectures and classification of neural networks. A neuron as a processor a classifier, methods of training, hard-learning problems
- Feed-forward networks, single- and multilayer perceptron. Learning: error back-propagation as iterative minimisation of the mean quadratic error
- Supervised and unsupervised learning. Knowledge generalisation, optimum degree of training
- Feed-back networks. Hopfield networks, behaviour, state diagram, attractors, learning. Networks with hidden nodes
- Application of relaxing minimisation of "energy" for optimisation problems, use of the network as associative memory. Stochastic neuron, Boltzmann machine, simulated annealing
- Recursive and Jordan networks. Competitive learning
- Kohonen maps, associative learning, automatic local organisation, refining of classification
- Possibilities of neuronal networks as signal processors and analysers, practical applications in processing and restoration of signals and images
- Optimum signal detection and restoration - approaches. Non-linear matched filters, effectivity comparison
- Deterioration models, LMS-filtering, diskrete Wiener filter in non-stationary environment
- Kalman filtering in scalar version, vector generalisation in stationary and non-stationary environment
- Adaptive filtering, adaptation algorithms, recursive realisation of adaptive filtering, filtering by method of stochastic gradients
- Typical applications of adaptive filtering. Comparison of concepts of optimum and adaptive filtering and neural-network oriented approach.
Course inclusion in study plans