Incremental Granular Fuzzy Modeling Using Imprecise Data Streams
نویسندگان
چکیده
System modeling in dynamic environments needs processing of streams 1 of sensor data and incremental learning algorithms. This paper suggests an incre2 mental granular fuzzy rule-based modeling approach using streams of fuzzy inter3 val data. Incremental granular modeling is an adaptivemodeling framework that uses 4 fuzzy granular data that originate from unreliable sensors, imprecise perceptions, or 5 description of imprecise values of a variable in the form fuzzy intervals. The incre6 mental learning algorithm builds the antecedent of functional fuzzy rules and the 7 rule base of the fuzzy model. A recursive least squares algorithm revises the para8 meters of a state-space representation of the fuzzy rule consequents. Imprecision in 9 data is accounted for using specificity measures. An illustrative example concerning 10 the Rossler attractor is given. 11
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