Evolving Linguistic Fuzzy Models from Data Streams

  • Daniel Leite
  • Fernando Gomide
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 271)


This work outlines a new approach for online learning from imprecise data, namely, fuzzy set based evolving modeling (FBeM) approach. FBeM is an adaptive modeling framework that uses fuzzy granular objects to enclose uncertainty in the data. The FBeM algorithm is data flow driven and supports learning on an instance-per-instance recursive basis by developing and refining fuzzy models on-demand. Structurally, FBeM models combineMamdani and functional fuzzy systems to output granular and singular approximations of nonstationary functions. In general, approximand functions can be time series, decision boundaries between classes, and control and regression functions. Linguistic description of the behavior of the system over time is provided by information granules and associated rules. An application example on a reactive control problem, underlining the complementarity of Mamdani and functional parts of the model, illustrates the usefulness of the approach. More specifically, the problem concerns sensor-based robot navigation and localization. In addition to precise singular output values, granular output values provide effective robust obstacle avoidance navigation.


Membership Function Data Stream Fuzzy Subset Granular System Granular Computing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.School of Electrical and Computer EngineeringUniversity of CampinasSao PauloBrazil

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