Faculty of Information Technology, BUT

Course details

Neural Networks, Adaptive and Optimum Filtering

QB4 Acad. year 2007/2008 Winter semester

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.


Language of instruction



Examination (oral)

Time span

39 hrs lectures

Assessment points

100 exam



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

Prerequisite kwnowledge and skills

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

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

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