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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/21460

Title: Learning stability features on sigmoid Fuzzy Cognitive Maps through a Swarm Intelligence approach
Authors: Napoles, Gonzalo
Bello, Rafael
Vanhoof, Koen
Issue Date: 2013
Publisher: Springer Berlin Heidelberg
Citation: Ruiz-Shulcloper, José; Sanniti di Baja, Gabriella (Ed.). Progress in Pattern Recognition, Image Analysis and Applications: 18th Iberoamerican Congress, CIARP 2013, Havana, Cuba, November 20-23, 2013, Proceedings, Part I, p. 270-277
Series/Report: Lecture Notes in Computer Science
Series/Report no.: 8258
Abstract: Fuzzy Cognitive Maps (FCM) are a proper knowledge-based tool for modeling and simulation. They are denoted as directed weighted graphs with feedback allowing causal reasoning. According to the transformation function used for updating the activation value of concepts, FCM can be grouped in two large clusters: discrete and continuous. It is notable that FCM having discrete outputs never exhibit chaotic states, but this premise can not be ensured for FCM having continuous output. This paper proposes a learning methodology based on Swarm Intelligence for estimating the most adequate transformation function for each map neuron (concept). As a result, we can obtain FCM showing better stability properties, allowing better consistency in the hidden patterns codified by the map. The performance of the proposed methodology is studied by using six challenging FCM concerning the field of the HIV protein modeling.
URI: http://hdl.handle.net/1942/21460
ISBN: 9783642418211
ISSN: 0302-9743
Category: C1
Type: Proceedings Paper
Appears in Collections: Research publications

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