Fuzzy Cognitive Maps (FCM), as defined originally, are limited in their capacity to model real-world scenarios, due to the rather simple representation of causal relationships between interrelated concepts. They can model a world that has only monotonic cause-effect relationships. Unlike this traditional FCM, which uses a linear function to represent the strength of relationship between two concepts, and a non-linear transfer function, to update the value of a concept during simulation, the FCM proposed by us uses fuzzy rules based on membership functions, and an aggregation operator respectively to serve these two purposes. This allows representation of non-monotonic causality, which is typical of many scenarios. © Springer-Verlag Berlin Heidelberg 2004.
CITATION STYLE
Shamim Khan, M., & Khor, S. W. (2004). A framework for fuzzy rule-based cognitive maps. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 3157, pp. 454–463). https://doi.org/10.1007/978-3-540-28633-2_49
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