Fuzzy Cognitive Maps for Applied Sciences and Engineering

From Fundamentals to Extensions and Learning Algorithms

  • Elpiniki I. Papageorgiou

Part of the Intelligent Systems Reference Library book series (ISRL, volume 54)

Table of contents

  1. Front Matter
    Pages i-xxvii
  2. Elpiniki I. Papageorgiou, Jose L. Salmeron
    Pages 1-28
  3. S. A. Gray, E. Zanre, S. R. J. Gray
    Pages 29-48
  4. O. Motlagh, S. H. Tang, F. A. Jafar, W. Khaksar
    Pages 49-64
  5. Athanasios Tsadiras, Nick Bassiliades
    Pages 65-87
  6. Alexander Yastrebov, Katarzyna Piotrowska
    Pages 133-144
  7. Grzegorz Słoń, Alexander Yastrebov
    Pages 145-157
  8. Márcio Mendonça, Lúcia Valéria Ramos de Arruda, Flávio Neves-Jr
    Pages 159-175
  9. Engin Yesil, Leon Urbas, Anday Demirsoy
    Pages 177-198
  10. Dimitri De Franciscis
    Pages 199-220
  11. Martin Wildenberg, Michael Bachhofer, Kirsten G. Q. Isak, Flemming Skov
    Pages 221-236
  12. Jose L. Salmeron, Elpiniki I. Papageorgiou
    Pages 237-252
  13. Ján Vaščák, Napoleon H. Reyes
    Pages 253-266
  14. M. Glykas
    Pages 291-318

About this book


Fuzzy Cognitive Maps (FCM) constitute cognitive models in the form of fuzzy directed graphs consisting of two basic elements: the nodes, which basically correspond to “concepts” bearing different states of activation depending on the knowledge they represent, and the “edges” denoting the causal effects that each source node exercises on the receiving concept expressed through weights. Weights take values in the interval [-1,1], which denotes the positive, negative or neutral causal relationship between two concepts. An FCM can be typically obtained through linguistic terms, inherent to fuzzy systems, but with a structure similar to the neural networks, which facilitates data processing, and has capabilities for training and adaptation.

During the last 10 years, an exponential growth of published papers in FCMs was followed showing great impact potential. Different FCM structures and learning schemes have been developed, while numerous studies report their use in many contexts with highly successful modeling results.


The aim of this book is to fill the existing gap in the literature concerning fundamentals, models, extensions and learning algorithms for FCMs in knowledge engineering. It comprehensively covers the state-of-the-art FCM modeling and learning methods, with algorithms, codes and software tools, and provides a set of applications that demonstrate their various usages in applied sciences and engineering.


Cognitive Systems Computational Algorithms Decision Making Fuzzy Cognitive Maps Fuzzy Systems Intelligent Systems Knowledge Representation Learning Algorithms Modelling Soft Computing

Editors and affiliations

  • Elpiniki I. Papageorgiou
    • 1
  1. 1.Department of Computer EngineeringTechnological Educational Institute of Central GreeceLamiaGreece

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-39739-4
  • Copyright Information Springer-Verlag Berlin Heidelberg 2014
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-39738-7
  • Online ISBN 978-3-642-39739-4
  • Series Print ISSN 1868-4394
  • Series Online ISSN 1868-4408
  • About this book