Boosting Fuzzy Rules for Nonlinear System Identification Through Unscented Kalman Filter

@inproceedings{Eftekhari2013BoostingFR,
  title={Boosting Fuzzy Rules for Nonlinear System Identification Through Unscented Kalman Filter},
  author={Mahdi Eftekhari and Malihe M. Farsangi and Mohsen Zeinalkhani},
  year={2013}
}
This paper presents a new hybrid methodology for learning Sugeno-type fuzzy models via subtractive clustering, Adaptive Boosting Regression (AdaBoostR) and Unscented Kalman Filter (UKF). The generated fuzzy models are used for modeling nonlinear benchmark processes. In the proposed procedure, first one fuzzy rule is generated by subtractive clustering algorithm from available data of a given nonlinear process. Then this fuzzy rule is considered as a base model and AdaBoostR is employed in order… CONTINUE READING

References

Publications referenced by this paper.
SHOWING 1-10 OF 16 REFERENCES

Boosting of granular models

  • Fuzzy Sets and Systems
  • 2006
VIEW 6 EXCERPTS
HIGHLY INFLUENTIAL

ANFIS: adaptive-network-based fuzzy inference system

  • IEEE Trans. Systems, Man, and Cybernetics
  • 1993
VIEW 3 EXCERPTS
HIGHLY INFLUENTIAL

Random sub spacing for regression ensembles

N. Rooney, D. Patterson, A. Tsymbal, S. Anand
  • Proc. 17th Int. FLAIRS Conf. on Artificial Intelligence, pp. 532-537, Mar. 2004.
  • 2004

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