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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/18670

Title: How to improve the convergence on sigmoid Fuzzy Cognitive Maps?
Authors: Napoles Ruiz, Gonzalo
Bello, Rafael
Vanhoof, Koen
Issue Date: 2014
Publisher: IOS PRESS
Citation: INTELLIGENT DATA ANALYSIS, 18 (S6), p. S77-S88
Abstract: Fuzzy Cognitive Maps (FCM) may be defined as Recurrent Neural Networks that allow causal reasoning. According to the transformation function used for updating the activation value of concepts they can be characterized as discrete or continuous. It is remarkable that FCM having discrete neurons never exhibit chaotic states, but this premise cannot be guaranteed for FCM having continuous concepts. On the other hand, complex Sigmoid FCM resulting from experts or learning algorithms often show chaotic or cyclic patterns, therefore leading to confusing interpretation of the investigated system. The first contribution of this paper is focused on explaining why most studies on FCM stability are not applicable to FCM used on classification or decision-making tasks. Next we describe a non-direct learning methodology based on Swarm Intelligence for improving the system stability once the causal weight estimation is done. The objective here is to find a specific threshold function for each map neuron simulating an external stimulus, instead of using the same transformation function for all concepts. At the end, we can compute more stable maps, so better consistency in hidden patterns is achieved.
Notes: [Napoles, Gonzalo; Bello, Rafael] Univ Cent Marta Abreu Las Villas, Santa Clara, Villa Clara, Cuba. [Napoles, Gonzalo; Vanhoof, Koen] Hasselt Univ, Hasselt, Belgium.
URI: http://hdl.handle.net/1942/18670
DOI: 10.3233/IDA-140710
ISI #: 000347782000007
ISSN: 1088-467X
Category: A1
Type: Journal Contribution
Validation: ecoom, 2016
Appears in Collections: Research publications

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