Evolving granular neural networks from fuzzy data streams

Daniel Leite, Pyramo Costa, Fernando Gomide

Research output: Contribution to journalArticlepeer-review

80 Scopus citations

Abstract

This paper introduces a granular neural network framework for evolving fuzzy system modeling from fuzzy data streams. The evolving granular neural network (eGNN) is able to handle gradual and abrupt parameter changes typical of nonstationary (online) environments. eGNN builds interpretable multi-sized local models using fuzzy neurons for information fusion. An online incremental learning algorithm develops the neural network structure from the information contained in data streams. We focus on trapezoidal fuzzy intervals and objects with trapezoidal membership function representation. More precisely, the framework considers triangular, interval, and numeric types of data to construct granular fuzzy models as particular arrangements of trapezoids. Application examples in classification and function approximation in material and biomedical engineering are used to evaluate and illustrate the neural network usefulness. Simulation results suggest that the eGNN fuzzy modeling approach can handle fuzzy data successfully and outperforms alternative state-of-the-art approaches in terms of accuracy, transparency and compactness.

Original languageEnglish
Pages (from-to)1-16
Number of pages16
JournalNeural Networks
Volume38
DOIs
StatePublished - Feb 2013
Externally publishedYes

Keywords

  • Evolving systems
  • Granular computing
  • Information fusion
  • Neurofuzzy networks
  • Online modeling

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