Belief Updating Recurrent Fuzzy Rules for Ordered Dataset Modelling
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概要
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We present a new method for modelling ordered datasets using Baldwin's mass assignment. This method generates a simplified memory-based fuzzy belief updating model. The predicted class fuzzy set naturally describes the current state of belief and the previous class fuzzy set defines how previous beliefs colour future beliefs. The model is implementaed using Fril evidential logic rules. Results are given in application to particle classification and facial feature detection. The models generated using this method are concise linguistically clear glass box models.
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