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Barragán, A. J., & Andújar, J. M. (2012). Fuzzy Logic Tools Reference Manual v1.0 (University
of Huelva). ISBN 978-84-15147-32-9, http://uhu.es/antonio.barragan/flt .
Barragán, A. J., Al-Hadithi, B. M., Jiménez, A., & Andújar, J. M. (2013). A general methodology
for on-line TS fuzzy modeling by the extended Kalman filter. Applied Soft Computing (in-press).
Barragán, A. J., Andújar, J. M., Aznar, M., & Jiménez, A. (2011a). Application of the extended
Kalman filter to fuzzy modeling: algorithms and practical implementation. In S. Galichet, J.
Montero & G. Mauris (Eds.), 7th conference of the European Society for Fuzzy Logic and Tech-
nology (EUSFLAT-2011) and LFA-2011, Advances in Intelligent Systems Research , (Vol. 1, pp.
691-698), (Aix-les-Bains, France), ISBN 978-90-78677-00-0. doi : 10.2991/eusflat.2011.23 .
Barragán, A. J., Andújar, J. M., Aznar, M., & Jiménez, A. (2011b). Methodology for adapting
the parameters of a fuzzy system using the extended Kalman filter. In S. Galichet, J. Mon-
tero & G. Mauris (Eds.), 7th conference of the European Society for Fuzzy Logic and Technol-
ogy (EUSFLAT-2011) and LFA-2011, Advances in Intelligent Systems Research (Aix-les-Bains,
France), (pp. 686-690). ISBN 978-90-78677-00-0, doi: 10.2991/eusflat.2011.65 .
Benmakrouha, F. (1997). Parameter identification in a fuzzy system with insufficient data. In IEEE
International Conference on Fuzzy Systems , (Vol. 1, pp. 537-542) (IEEE, Piscataway, NJ, United
States). doi: 10.1109/FUZZY.1997.616424 .
Bezdek, J. C., & Dunn, J. C. (1975). Optimal fuzzy partitions: A heuristic for estimating the
parameters in a mixture of normal distributions. IEEE Transactions on Computers , C-24 (8),
835-838.
Bezdek, J. C. (1981). Pattern recognition with fuzzy objective function algorithms . Norwell, MA,
USA: Kluwer Academic Publishers.
Chafaa, K., Ghanaï, M., & Benmahammed, K. (2007). Fuzzy modelling using Kalman filter. IET
Control Theory and Applications , 1 (1), 58-64. doi: 10.1049/iet-cta:20050268 .
Chiu, S. (1994). Fuzzy model identification based on cluster estimation. Journal of Intelligent and
Fuzzy Systems , 2 , 267-278.
Denaï, M. A., Palis, F., & Zeghbib, A. H. (2007). Modeling and control of non-linear systems using
soft computing techniques. Applied Soft Computing , 7 (3), 728-738. doi: 10.1016/j.asoc.2005.12.
005 .
Dunn, J. C. (1973). A fuzzy relative of the ISODATA process and its use in detecting compact
well-separated clusters. Journal of Cybernetics , 3 , 32-57.
García-Cerezo, A., Ollero, A., & Aracil, J. (1994). Dynamic analysis of weigthed-output fuzzy
control systems. Annual Review in Automatic Programming , 19 (C), 43-48. doi: 10.1016/0066-
4138(94)90040-X .
Gordillo, F., Aracil, J., &Alamo, T. (1997). Determining limit cycles in fuzzy control systems. IEEE
International Conference on Fuzzy Systems , 1 , 193-198. doi: 10.1109/FUZZY.1997.616367 .
Grewal, M. S., & Andrews, A. P. (2001). Kalman filtering: Theory and practice using MATLAB
(2nd ed.). New York: Wiley. ISBN 0-471-39254-5.
Gustafson, D., & Kessel, W. (1979). Fuzzy clustering with fuzzy covariance matrix. In M. Gupta,
R. Ragade & R. R Yager (Eds.), Advances in fuzzy set theory and applications (pp. 605-620).
Amsterdam: North-Holland.
Horikawa, S.-I., Furuhashi, T., & Uchikawa, Y. (1992). On fuzzy modeling using fuzzy neural
networks with the back-propagation algorithm. IEEE Transactions on Neural Networks , 3 (5),
801-806. doi : 10.1109/72.159069 .
Jang, J.-S. R. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions
on Systems, Man, and Cybernetics , 23 (3), 665-685. doi: 10.1109/21.256541 .
Jang, J.-S. R., & Sun, C.-T. (1995). Neuro-fuzzy modeling and control. Proceedings of the IEEE ,
83 (3), 378-406. doi: 10.1109/5.364486 .
Jiang, T., & Li, Y. T. (1996). Generalized defuzzification strategies and their parameter learning
procedures. IEEE Transactions on Fuzzy Systems , 4 (1), 64-71. doi : 10.1109/91.481845 .
Jiménez, A., Aroba, J., de la Torre, M. L. d. l., Andújar, J. M., & Grande, J. A. (2009). Model of
behaviour of conductivity versus pH in acid mine drainage water, based on fuzzy logic and data
mining techniques. Journal of Hydroinformatics , 2 (11), 147-153. doi: 10.2166/hydro.2009.015 .
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