Learning of Fuzzy Cognitive Maps for simulation and knowledge discovery

In recent years Fuzzy Cognitive Maps (FCM) has become a useful Soft Computing technique for modeling and simulation. They are connectionist and recurrent structures involving concepts describing the system behavior, and causal connections. This paper describes two abstract models based on Swarm Intelligence for learning parameters characterizing FCM, which is a central issue on this field. At the end, we obtain accurate maps, allowing the simulation of the system and also the extraction of relevant knowledge associated with underlying patterns.

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