Course details
Neural networks, adaptive and optimum filtering
DQB4 FEKT DQB4 Acad. year 2002/2003 Winter semester 4 credits
In its first part, the course is devoted to providing an overview of types of architecture of neural networks and to a detailed analysis of their properties. Applications of neural networks in signal and image processing and recognition are included in this treatment. In the second part, the course deals with the theory of optimum detection and restoration of signals in its classical and generalised forms, emphasising the common base of this whole area. The subject highlights the common view-points in the area of neural networks and in the area of optimised signal processing.
Guarantor
Language of instruction
Completion
Time span
- 39 hrs lectures
Department
Subject specific learning outcomes and competences
Theoretical knowledge of areas of neural networks and optimum signal processing, ability to apply and, if necessary, to modify these methods for concrete problems.
Learning objectives
Gaining knowledge of concepts of the theory of neural networks and theory of adaptive and optimum filtering, discussing the features of individual methods and showing common view-points of both areas
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
Progress assessment
oral exam
Course inclusion in study plans