Inference using Binary, Trivalent and Sigmoid Fuzzy Cognitive Maps
نویسنده
چکیده
In this paper, we compare the inference capabilities of three different types of Fuzzy Cognitive Maps. A Fuzzy Cognitive Map is a Recurrent Artificial Neural Network that creates models as collections of concepts/neurons and the various causal relations that exist between these concepts/neurons. The three different types of Fuzzy Cognitive Maps that we study are the Binary, the Trivalent and the Sigmoid Fuzzy Cognitive Map, each of them using the corresponding transfer function for their neurons/concepts. Predictions are made by viewing dynamically the consequences of the various imposed scenarios. The prediction making capabilities are examined and presented using a Fuzzy Cognitive Map concerning the Public Health of a city. Conclusions are drawn for the use of the three types of Fuzzy Cognitive Maps for making prediction.
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