Evolutionary Development of Fuzzy Cognitive Maps

Fuzzy cognitive maps (FCMs) form a convenient, simple, and powerful tool for simulation and analysis of dynamic systems. The popularity of FCMs stems from their simplicity and transparency. While being successful in a variety of application domains, FCMs are hindered by necessity of involving domain...

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Bibliographic Details
Published inThe 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05 pp. 619 - 624
Main Authors Stach, W., Kurgan, L., Pedrycz, W., Reformat, M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2005
Subjects
Analytical models
Application software
Circuit analysis
Computational modeling
Computer simulation
Failure analysis
Fuzzy cognitive maps
Genetic algorithms
Humans
Learning systems
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Summary:Fuzzy cognitive maps (FCMs) form a convenient, simple, and powerful tool for simulation and analysis of dynamic systems. The popularity of FCMs stems from their simplicity and transparency. While being successful in a variety of application domains, FCMs are hindered by necessity of involving domain experts to develop the model. Since human experts are subjective and can handle only relatively simple networks (maps), there is an urgent need to develop methods for automated generation of FCM models. This study proposes a novel evolutionary learning that is able to generate FCM models from input historical data, and without any human intervention. The proposed method is based on genetic algorithms, and is carried out through supervised learning. The paper tests the method through a series of carefully selected experimental studies
ISBN:0780391594
9780780391598
ISSN:1098-7584
DOI:10.1109/FUZZY.2005.1452465