Evolving granular neural network for fuzzy time series forecasting

@article{Leite2012EvolvingGN,
  title={Evolving granular neural network for fuzzy time series forecasting},
  author={Daniel F. Leite and Pyramo Pires da Costa and F. Gomide},
  journal={The 2012 International Joint Conference on Neural Networks (IJCNN)},
  year={2012},
  pages={1-8}
}
  • D. LeiteP. CostaF. Gomide
  • Published 10 June 2012
  • Computer Science
  • The 2012 International Joint Conference on Neural Networks (IJCNN)
A primary requirement of a broad class of evolving intelligent systems is to process a sequence of numeric data over time. This paper introduces a granular neural network framework for evolving fuzzy system modeling from fuzzy data streams. The evolving granular neural network (eGNN) efficiently handles concept changes, distinctive events of nonstationary environments. eGNN constructs interpretable multi-sized local models using fuzzy neural information fusion. An incremental learning algorithmā€¦Ā 
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