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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/25542

Title: On the Accuracy-Convergence Tradeoff in Sigmoid Fuzzy Cognitive Maps
Authors: Napoles, Gonzalo
Concepción, Leonardo
Falcon, Rafael
Bello, Rafael
Vanhoof, Koen
Issue Date: 2017
Citation: IEEE transactions on fuzzy systems, 26 (4), p. 2479-2484
Status: In Press
Abstract: Recently, a learning procedure to improve the convergence of sigmoid Fuzzy Cognitive Maps was proposed. This algorithm estimates the slope of each sigmoid neuron while preserving the causal weights. This paper proposes a more realistic error function for this algorithm, which is based on i) the dissimilarity between two consecutive responses, and ii) the dissimilarity between the current output and the expected one. As a second contribution, we introduce four sufficient conditions to arrive at stability features. These conditions allow assessing the accuracy-convergence tradeoff attached to the proposed learning procedure.
Notes: Napoles, G (reprint author), Hasselt Univ, Fac Business Econ, B-3500 Hasselt, Belgium. gonzalo.napoles@uhasselt.be; lcperez@uclv.cu; rfalc032@uottawa.ca; rbellop@uclv.edu.cu; koen.vanhoof@uhasselt.be
URI: http://hdl.handle.net/1942/25542
DOI: 10.1109/TFUZZ.2017.2768327
ISI #: 000440798300059
ISSN: 1063-6706
Category: A1
Type: Journal Contribution
Appears in Collections: Research publications

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