Fuzzy Cognitive Maps Based Models for Pattern Classification: Advances and Challenges

Gonzalo Nápoles*, Maikel Léon, Isel Grau, Koen Vanhoof, Rafael Bello

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review


Fuzzy Cognitive Maps (FCMs) have proven to be a suitable methodology for the design of knowledge-based systems. By combining both uncertainty depiction and cognitive mapping, this technique represents the knowledge of systems that are characterized by ambiguity and complexity. In short, FCMs can be defined as recurrent neural networks that include elements of fuzzy logic during the knowledge engineering phase. While the literature contains many studies claiming how this Soft Computing technique is able to model complex and dynamical systems, we explore another promising research field: the use of FCMs in solving pattern classification problems. This is motivated by the transparency of the decision model attached to these cognitive, neural networks. In this chapter, we revise some prominent advances in the area of FCM-based classifiers and open challenges to be confronted.
Original languageEnglish
Title of host publicationSoft Computing Based Optimization and Decision Models
EditorsDavid A. Pelta, Carlos Cruz Corona
Place of PublicationCham
PublisherSpringer International Publishing
Number of pages16
ISBN (Print)978-3-319-64286-4
Publication statusPublished - 2018
Externally publishedYes


Dive into the research topics of 'Fuzzy Cognitive Maps Based Models for Pattern Classification: Advances and Challenges'. Together they form a unique fingerprint.

Cite this