HYBRID ALGORITHM FOR FUZZY MODEL PARAMETER ESTIMATION BASED ON GENETIC ALGORITHM AND DERIVATIVE BASED METHODS

A. Lavygina, I. Hodashinsky

Abstract

Hybrid method for estimation of fuzzy model parameters is presented. The main idea of the method is to apply gradient descent method or Kalman filter as a mutation operator of genetic algorithm for estimation of antecedent parameters of fuzzy “IF-THEN” rules. Thus, part of the individuals in the population mutate by means of gradient descent method or Kalman filter, the others mutate in an ordinary way. Once antecedents are tuned, consequents tuning is performed with the least squares method. The results of computer experiment are presented.

References

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Paper Citation


in Harvard Style

Lavygina A. and Hodashinsky I. (2011). HYBRID ALGORITHM FOR FUZZY MODEL PARAMETER ESTIMATION BASED ON GENETIC ALGORITHM AND DERIVATIVE BASED METHODS . In Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: FCTA, (IJCCI 2011) ISBN 978-989-8425-83-6, pages 513-515. DOI: 10.5220/0003690605130515


in Bibtex Style

@conference{fcta11,
author={A. Lavygina and I. Hodashinsky},
title={HYBRID ALGORITHM FOR FUZZY MODEL PARAMETER ESTIMATION BASED ON GENETIC ALGORITHM AND DERIVATIVE BASED METHODS},
booktitle={Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: FCTA, (IJCCI 2011)},
year={2011},
pages={513-515},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003690605130515},
isbn={978-989-8425-83-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: FCTA, (IJCCI 2011)
TI - HYBRID ALGORITHM FOR FUZZY MODEL PARAMETER ESTIMATION BASED ON GENETIC ALGORITHM AND DERIVATIVE BASED METHODS
SN - 978-989-8425-83-6
AU - Lavygina A.
AU - Hodashinsky I.
PY - 2011
SP - 513
EP - 515
DO - 10.5220/0003690605130515