Course detail

Neural Networks, Adaptive and Optimum Filtering

FIT-QB4Acad. year: 2010/2011

Not applicable.

Language of instruction

Czech

Number of ECTS credits

0

Mode of study

Not applicable.

Guarantor

prof. Ing. Jiří Jan, CSc.

Department

Department of Biomedical Engineering (UBMI)

Learning outcomes of the course unit

Not applicable.

Prerequisites

Not applicable.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

Not applicable.

Course curriculum

Not applicable.

Work placements

Not applicable.

Aims

Not applicable.

Specification of controlled education, way of implementation and compensation for absences

Not applicable.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic 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

Recommended reading

  • 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

Classification of course in study plans

  • Programme VTI-DR-4 Doctoral

    branch DVI4 , any year of study, winter semester, elective

  • Programme VTI-DR-4 Doctoral

    branch DVI4 , any year of study, winter semester, elective

Type of course unit

 

Lecture

39 hours, optionally

Teacher / Lecturer

prof. Ing. Jiří Jan, CSc.

Syllabus

  • 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.