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Structure Optimization and Learning of Fuzzy Cognitive Map with the Use of Evolutionary Algorithm and Graph Theory Metrics

  • Katarzyna PoczetaEmail author
  • Łukasz Kubuś
  • Alexander Yastrebov
Chapter
  • 193 Downloads
Part of the Studies in Computational Intelligence book series (SCI, volume 795)

Abstract

Fuzzy cognitive map (FCM) allows to discover knowledge in the form of concepts significant for the analyzed problem and causal connections between them. The FCM model can be developed by experts or using learning algorithms and available data. The main aspect of building of the FCM model is concepts selection. It is usually based on the expert knowledge. The aim of this paper is to present the developed evolutionary algorithm for structure optimization and learning of fuzzy cognitive map on the basis of available data. The proposed approach allows to select key concepts during learning process based on metrics from the area of graph theory: significance of each node, total value of a node and total influence of the concept and determine the weights of the connections between them. A simulation analysis of the developed algorithm was done with the use of synthetic and real-life data.

Keywords

Fuzzy Cognitive Maps (FCM) Graph Theory Metrics Total Influence Concept Selection Real-life Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Katarzyna Poczeta
    • 1
    Email author
  • Łukasz Kubuś
    • 1
  • Alexander Yastrebov
    • 1
  1. 1.Kielce University of TechnologyKielcePoland

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