Recurrent Fuzzy Rules for Belief Updating

In this paper we present a simple belief updating system using recurrent fuzzy rules which improves class prediction in ordered datasets. The recurrent fuzzy rule builds up belief in a class for each point in a sample-ordered or timeordered dataset. Belief in each class is represented by a fuzzy set predicted class defined on the class universe. Belief in a class increases as positive cases are presented and decreases with negative cases. We show how the importance and characteristics of belief updating are determined through the generation of reference previous class fuzzy sets and evidential logic rule weights using mass assignment and semantic discrimination analysis. Performance of the recurrent belief updating rule is compared to the non-recurrent rule in application to facial feature detection and the classification of particles in gaseous streams.