Incremental granular fuzzy modeling using imprecise data streams

Daniel Leite, Fernando Gomide

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


System modeling in dynamic environments needs processing of streams of sensor data and incremental learning algorithms. This paper suggests an incremental granular fuzzy rule-based modeling approach using streams of fuzzy interval data. Incremental granular modeling is an adaptivemodeling framework that uses fuzzy granular data that originate from unreliable sensors, imprecise perceptions, or description of imprecise values of a variable in the form fuzzy intervals. The incremental learning algorithm builds the antecedent of functional fuzzy rules and the rule base of the fuzzy model. A recursive least squares algorithm revises the parameters of a state-space representation of the fuzzy rule consequents. Imprecision in data is accounted for using specificity measures. An illustrative example concerning the Rossler attractor is given.

Original languageEnglish
Pages (from-to)107-124
Number of pages18
JournalStudies in Fuzziness and Soft Computing
StatePublished - 2015
Externally publishedYes


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