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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/21849

Title: A computational tool for simulation and learning of Fuzzy Cognitive Maps
Authors: Nápoles, Gonzalo
Grau, Isel
Bello, Rafael
Léon, Maikel
Vanhoof, Koen
Papageorgiou, Elpiniki
Issue Date: 2015
Publisher: IEEE
Citation: Yazici, A.; Pal, N.R.; Kaymak, U.; Martin, T.; Ishibuchi, H.; Lin, C.T.; Sousa, J.M.C.; Tutmez, B. (Ed.). IEEE International Fuzzy Systems Conference Proceedings (FUZZ-IEEE 2015)
Series/Report: IEEE International Fuzzy Systems Conference
Abstract: During the last decade Fuzzy Cognitive Maps (FCM) have become a useful tool for solving unstructured problems. In a few words they could be defined as Recurrent Neural Networks for simulating complex systems, where neurons denote concepts, objects or entities of the investigated system. Normally FCM are entirely designed using the best knowledge of a group of experts in a given domain, so frequently learning algorithms for tuning the model parameters are required. Despite the theoretical advances in such fields, the lack of a suitable computational framework for handling FCM-based systems is still an open problem. This paper introduces a novel tool for designing and simulating FCM which gathers several learning algorithms for adjusting the introduced parameters. More specifically, the framework includes supervised and unsupervised learning algorithms for computing the causal weights, algorithms for optimizing the network topology in large FCM (without losing significant information) and also methods for improving the global convergence on continuous FCM. It should be stated that these algorithms are oriented to prediction tasks, but they could be easily extended to other fields.
Notes: [Napoles, Gonzalo; Grau, Isel; Bello, Rafael] Cent Univ Las Villas, Dept Comp Sci, Santa Clara, Cuba. [Leon, Maikel; Vahoof, Koen] Hasselt Univ, Dept Business Informat, Diepenbeek, Belgium. [Papageorgiou, Elpiniki] Technol Educ Inst Cent Greece, Dept Comp Engn, Lamia, Greece.
URI: http://hdl.handle.net/1942/21849
DOI: 10.1109/FUZZ-IEEE.2015.7337859
ISI #: 000370288300060
ISBN: 9781467374286
ISSN: 1544-5615
Category: C1
Type: Proceedings Paper
Validation: ecoom, 2017
Appears in Collections: Research publications

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