Course detail

Neural Networks, Adaptive and Optimum Filtering

FIT-QB4Acad. year: 2019/2020

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.

Learning outcomes of the course unit

Theoretical knowledge of areas of neural networks and optimum signal processing, ability to apply and, if necessary, to modify these methods for concrete problems.

Prerequisites

signal and system theory, digital signal processing (e.g. the subjects BCZA, MMZS)

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

Jan, J,: Číslicová filtrace, analýza a restaurace signálů. 2. rozš. vydání. VUTIUM Brno 2003
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

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

Not applicable.

Language of instruction

Czech, English

Work placements

Not applicable.

Aims

Gaining knowledge of theory of neural networks and theory of adaptive and optimum filtering, showing common view-points of both areas

Classification of course in study plans

  • Programme VTI-DR-4 Doctoral

    branch DVI4 , any year of study, summer semester, 0 credits, optional

  • Programme VTI-DR-4 Doctoral

    branch DVI4 , any year of study, summer semester, 0 credits, optional

  • Programme VTI-DR-4 Doctoral

    branch DVI4 , any year of study, summer semester, 0 credits, optional

  • Programme VTI-DR-4 Doctoral

    branch DVI4 , any year of study, summer semester, 0 credits, optional

Type of course unit

 

Lecture

39 hours, optionally

Teacher / Lecturer

eLearning