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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 |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
manuscript.pdf | Peer-reviewed author version | 1.02 MB | Adobe PDF | View/Open |
10.1142@S0218213018600102.pdf Restricted Access | Published version | 1.3 MB | Adobe PDF | View/Open Request a copy |
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