Methods and Algorithms for Fuzzy Cognitive Map-based Modeling
The challenging problem of complex systems modeling methods with learning capabilities and characteristics that utilize existence knowledge and human experience is investigated using Fuzzy Cognitive Maps (FCMs). FCMs are ideal causal cognition tools for m
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Methods and Algorithms for Fuzzy Cognitive Map-based Modeling Elpiniki I. Papageorgiou and Jose L. Salmeron
Abstract The challenging problem of complex systems modeling methods with learning capabilities and characteristics that utilize existence knowledge and human experience is investigated using Fuzzy Cognitive Maps (FCMs). FCMs are ideal causal cognition tools for modeling and simulating dynamic systems. Their usefulness has been proved from their wide applicability in diverse domains. They gained momentum due to their simplicity, flexibility to model design, adaptability to different situations, and ease of use. In general, they model the behavior of a complex system utilizing experts knowledge and/or available knowledge from existing databases. They are mainly used for knowledge representation and decision support where their modeling features and their learning capabilities make them efficient to support these tasks. This chapter gathers the methods and learning algorithms of FCMs applied to modeling and decision making tasks. A comprehensive survey of the current modeling methodologies and learning algorithms of FCMs is presented. The leading methods and learning algorithms, concentrated on modeling, are described analytically and analyzed presenting experimental results of a known case study. The main features of computational methodologies are compared and future research directions are outlined.
Electronic supplementary material The online version of this article (doi: 10.1007/978-3642-39739-4_1) contains supplementary material, which is available to authorized users. E. I. Papageorgiou (B) Department of Computer Engineering, Technological Educational Institute of Central Greece, 3rd Km Old National Road Lamia-Athens, 35100 Lamia, Greece e-mail: [email protected] J. L. Salmeron Computational Intelligence Lab, University Pablo de Olavide, 1st km. Utrera Road, Seville, Spain e-mail: [email protected] E. I. Papageorgiou (ed.), Fuzzy Cognitive Maps for Applied Sciences and Engineering, Intelligent Systems Reference Library 54, DOI: 10.1007/978-3-642-39739-4_1, © Springer-Verlag Berlin Heidelberg 2014
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E. I. Papageorgiou and J. L. Salmeron
1 Introduction Fuzzy Cognitive Map (FCM) is a method for modeling complex systems utilizing existence knowledge and human experience. It has learning capabilities and characteristics which improve its structure and computational behavior [39, 44, 63]. It was introduced by Kosko [31], as an extension to cognitive maps [10], providing a powerful machinery for modeling of dynamical systems. As a knowledge representation and reasoning technique, it depicts a system in a form that corresponds closely to the way humans perceive it. Also, it is able to incorporate experts’ knowledge and available knowledge from data in the form of rules [44, 63, 69, 71]. This approach represents knowledge by emphasizing causal connections and map structure. The resulting fuzzy model is used to analyze, simulate, and test the influence of parameters and predict system behavior. The F
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