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Incremental Granular Fuzzy Modeling Using Imprecise Data Streams

  • Daniel Leite
  • Fernando Gomide
Chapter
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 326)

Abstract

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 adaptive modeling 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.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Federal University of LavrasDepartment of Engineering, Control and Automation Research GroupMinas GeraisBrazil
  2. 2.University of CampinasSchool of Electrical and Computer EngineeringSao PauloBrazil

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