Identification of evolving fuzzy rule-based models

@article{Angelov2002IdentificationOE,
  title={Identification of evolving fuzzy rule-based models},
  author={Plamen P. Angelov and Richard A. Buswell},
  journal={IEEE Trans. Fuzzy Systems},
  year={2002},
  volume={10},
  pages={667-677}
}
An approach to identification of evolving fuzzy rule-based (eR) models is proposed in this paper. eR models implement a method for the noniterative update of both the rule-base structure and parameters by incrementalunsupervised learning. The rule-base evolves by adding more informative rules than those that previously formed the model. In addition, existing rules can be replaced with new rules based on ranking using the informative potential of the data. In this way, the rule-base structure is… CONTINUE READING
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References

Publications referenced by this paper.
Showing 1-10 of 34 references

Rule-Based Models: A Tool for Design of Flexible Adaptive Systems, In the Series Studies in Fuzziness and Soft Computing

  • P. Angelov, Evolving
  • 2002
Highly Influential
10 Excerpts

Predicting the Mackey-Glass timeseries with Cascade-correlation learning,

  • R. S. Crowder
  • inProc. of Connectionist Models Summer School ,
  • 1990
Highly Influential
6 Excerpts

EUNITE : European Network on Intelligent Technologies for Smart Adaptive Systems

  • R. S. Crowder
  • 2001

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