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[58850] Artykuł: Learning Fuzzy Cognitive Maps Using Evolutionary Algorithm Based on System Performance IndicatorsCzasopismo: Advances in Intelligent Systems and Computing - Automation 2017 Innovations in Automation, Robotics and Measurement Techniques Tom: 550, Strony: 554-564ISSN: 2194-5357 ISBN: 978-3-319-54042-9 Wydawca: SPRINGER INTERNATIONAL PUBLISHING AG, GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND Opublikowano: 2017 Seria wydawnicza: Advances in Intelligent Systems and Computing Autorzy / Redaktorzy / Twórcy Grupa MNiSW: Materiały z konferencji międzynarodowej (zarejestrowane w Web of Science) Punkty MNiSW: 15 Klasyfikacja Web of Science: Proceedings Paper ![]() ![]() ![]() Keywords: Fuzzy cognitive map  Evolutionary algorithm  System performance indicators  |
Fuzzy cognitive map (FCM) is a soft computing technique for modeling decision support systems. Construction of the FCM model is based on the selection of concepts important for the analyzed problem and determining significant connections between them. Fuzzy cognitive map can be initialized based on expert knowledge or automatic constructed from data with the use of supervised or evolutionary learning algorithm. FCM models learned from data are much denser than those created by experts. This paper proposes a new evolutionary approach for fuzzy cognitive maps learning based on system performance indicators. The learning process has been carried out with the use of Elite Genetic Algorithm and Individually Directional Evolutionary Algorithm. The developed approach allows to receive FCM model more similar to the reference system than standard methods for fuzzy cognitive maps learning.