INEA00109W, INEA00109P


 Supervisor: Jacek Mazurkiewicz, PhD



(Hours / sem. (h))




Knowledge of artificial neural networks in pattern recognition, digital signals and data
processing: topology of networks, influence of parameters for network behavior. Genetic
algorithms used for data pre- and postprocessing. Expert systems – reasoning rules and knowledge
base creation for different tasks.



Idea of intelligent processing. Fuzzy sets and approximate reasoning. Expert systems -
knowledge base and reasoning rules. Organization of expert systems. Artificial neural networks –
architecture of typical structures: MLP, Kohonen, Hopfield, Hamming nets, learning and retrieving
algorithms, applications. Genetic algorithms – description, classification, examples of applications.


  • Idea of intelligent processing
  • Fuzzy sets and approximate reasoning
  • Expert systems - knowledge base organization
  • Expert systems - reasoning rules creation
  • Expert systems: typical organization and applications
  • Artificial neural networks: learning and retrieving algorithms
  • Multilayer percetpron
  • Kohonen neural network
  • Hopfield neural network
  • Hamming neural network
  • Artificial neural networks: applications
  • Genetic algorithms: description and classification
  • Genetic algorithms: basic mechanisms and solutions



Exercises of using artificial neural networks as well as genetic algorithms in different tasks about
pattern recognition, digital signals and data processing. The realization includes the changes
of network topology tests and the influence of parameters for network behavior.
Expert systems creation to dedicated problems.


Conditions of the course acceptance/credition:

Realization, presentation and discussion about the results of the project exercises.

The subjects of the project exercises are defined in general during the first project

meeting. The details related to the subjects of project exercises are presented step by step

when the previous subjects are over. Positive result of the test focused on the knowledge

presented during lectures. The test is at the end of semester. The precision date of the test

is defined during the first lecture.



  1. B. Bouchon Meunier, Fuzzy Logic and Soft Computing

  2. O. Castilo, A. Bonarini, Soft Computing Applications

  3. M. Caudill, Ch. Butler, Understanding Neural Networks

  4. E. Damiani, Soft Computing in Software Engineering

  5. R. Hecht-Nielsen, Neurocomputing
  6. S. Y. Kung, Digital Neural Networks

  7. D. K. Pratihar, Soft Computing

  8. S. N. Sivanandam, S. N. Deepa, Principles of Soft Computing

  9. A. K. Srivastava, Soft Computing

10. D. A. Waterman, A Guide to Expert Systems
11. D. Zhang, Parallel VLSI Neural System Design