Production Rule and Network Structure Models for Knowledge Extraction from Complex Processes Under Uncertainty

  • Boriana VatchovaEmail author
  • Alexander Gegov
Part of the Studies in Computational Intelligence book series (SCI, volume 657)


This paper considers processes with many inputs and outputs from different application areas. Some parts of the inputs are measurable and others are not because of the presence of stochastic environmental factors. This is the reason why processes of this kind operate under uncertainty. As some factors cannot be measured and reflected into the process model, data mining methods cannot be applied. The proposed approach which can be applied in this case is based on artificial intelligence methods[1].


  1. 1.
    Lee, J. (ed.): Software Engineering with Computational Intelligence, Studies in Fuzziness and Soft Computing. Springer (2003)Google Scholar
  2. 2.
    Gray, J., Research, M., Han, J., Kamber, M.: Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems)”, 2nd edn. Series Editors by Elsevier Inc. (2006)Google Scholar
  3. 3.
    Ruan, D., Chen, G., Kerre, E., West, G. (eds.): Intelligent Data Mining: Techniques and Applications (Studies in Computational Intelligence). Springer, Berlin, Heidelberg (2010)Google Scholar
  4. 4.
    Larose, D.: Data Mining Methods and Modles. A Wiley. New Jersey, Canada (2006)Google Scholar
  5. 5.
    Han, J., Kamber, M.: Data Mining Techniques. Morgan Kaufmann Publisher (2005)Google Scholar
  6. 6.
    Kandel, A., Last, M., Bunke, H.: Data Mining and Computational Intelligence. Physical-Verlag, Heidelberg (2001)Google Scholar
  7. 7.
    Kuznecov, V., Adelon-Velski, G.: Discrete mathematics for engineers. Moscow, Energoatomizdat (in Russian) (1998)Google Scholar
  8. 8.
    Lapa, V.: Mathematical bases of cybernetics. Kiev, Visha Shkola (1974) (in Russian)Google Scholar
  9. 9.
    Gotvald, S.: Multi-valued Logic. Introduction to Fuzzy Methods. Theory and Applications. Akademy–Ferlag (1989) (in German)Google Scholar
  10. 10.
    Vatchova, B.: Derivation and Assessment of Reliability of Knowledge for Multifactor Industrial Processes”, PhD Thesis, 167 pages, Bulgarian Academy of Sciences, Sofia (2009) (in Bulgarian)Google Scholar
  11. 11.
    Gegov, E.A., Vatchova, B., Gegov, E.D.: Multi-valued Method for Knowledge Extraction and Updating in Real Time. IEEE’04, vol. 2, pp. 17-6–17-8. Varna, Bulgaria (2008)Google Scholar
  12. 12.
    Gegov, E., Vatchova, B.: Extraction of knowledge for complex objects from experimental data using functions of multi-valued logic. In: European Conference on Complex Systems ‘09, University of Warwick, Coventry, UK, 21–25 Sept 2009Google Scholar
  13. 13.
    Gegov, E.: Methods and Applications into Computer Intelligence and Information Technologies of Control Systems. Publisher “St. Ivan Rilsky”, Sofia (2003) (in Bulgarian)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.Institute of Information and Communication Technologies, Bulgarian Academy of SciencesSofiaBulgaria
  2. 2.School of ComputingUniversity of PortsmouthPortsmouthUK

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