Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/27948
Title: FCM Expert: Software Tool for Scenario Analysis and Pattern Classification Based on Fuzzy Cognitive Maps
Authors: NAPOLES RUIZ, Gonzalo 
LEON ESPINOSA, Maikel 
Grau, Isel
VANHOOF, Koen 
Issue Date: 2018
Source: International Journal of Artificial Intelligence Tools, 27 (07)
Abstract: Fuzzy Cognitive Maps (FCMs) have become a suitable and proven knowledge-based methodology for systems modeling and simulation. This technique is especially attractive when modeling systems characterized by ambiguity, and/or non-trivial causalities among its variables. The rich literature that is found related to FCMs reports very clearly many successful studies solved through the use of FCMs; however, when it comes to software implementations, where domain experts can design FCM-based systems, run simulations or perform more advanced experiments, not much is found or documented. The few existing implementations are not proficient in providing options for experimentation. Therefore, we believe that a gap exists, specifically between the theoretical advances and the development of accurate, transparent and sound FCM-based systems; and we advocate for the creation of more complete and flexible software products. The goal of this paper is to introduce "FCM Expert", a software tool for fuzzy cognitive modeling, where we focus on scenario analysis and pattern classification. The main features of FCM Expert rely on Machine Learning algorithms to compute the parameters that might define a model, optimize its network topology and improve the system convergence without losing information. Also, FCM Expert allows performing WHAT-IF simulations and studying the system behavior through a friendly, intuitive and easy-to-use graphical user interface.
Notes: Napoles, G (reprint author), Univ Hasselt, Fac Business Econ, Hasselt, Belgium. napoles.gonzalo@gmail.com; mleon@bus.miami.edu
Keywords: Fuzzy cognitive maps; software tool; scenario analysis; pattern classification; machine learning algorithms
Document URI: http://hdl.handle.net/1942/27948
ISSN: 0218-2130
e-ISSN: 1793-6349
DOI: 10.1142/S0218213018600102
ISI #: 000450114500004
Category: A1
Type: Journal Contribution
Validations: ecoom 2019
Appears in Collections:Research publications

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