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2.5 Conclusions
In this chapter the application of extended Kalman filter (EKF) for the parametric
adaptation of a TS fuzzy model is presented, which allows obtaining accurate models
without renounce the computational efficiency that characterizes the Kalman filter,
and allows its implementation online with the process. It is also proposed two algo-
rithms to adjust the antecedents and consequents of a TS fuzzy model, and have been
presented several examples.
Acknowledgments The authors would like to thank the Spanish Ministry of Economy and Com-
petitiveness for its support to this work through projects DPI2010-17123 and DPI2010-21247-C02-
01, the Regional Government of Andalusia (Spain) for supporting TEP-6124 project, as well as the
European Union Regional Development for funding the last two projects.
References
Al-Hadithi, B. M., Jiménez, A., &Matía, F. (2012). A new approach to fuzzy estimation of Takagi-
Sugeno model and its applications to optimal control for nonlinear systems. Applied Soft Com-
puting , 12 , 280-290.
Al-Hadithi, B. M., Matía, F., & Jiménez, A. (2007). Análisis de estabilidad de sistemas borrosos.
Revista Iberoamericana de Automática e Informática Industrial (RIAI) , 4 (2), 7-25.
Andújar, J. M., &Barragán, A. J. (2010). 2010 IEEE International Conference on A formal method-
ology for the analysis and design of nonlinear fuzzy control systems, in Fuzzy Systems (FUZZ) ,
1 (Barcelona, Spain), (pp. 66-74). doi: 10.1109/FUZZY.2010.5583980 .
Andújar, J. M., & Barragán, A. J. (2005). A methodology to design stable nonlinear fuzzy control
systems. Fuzzy Sets and Systems , 154 (2), 157-181. doi: 10.1016/j.fss.2005.03.006 .
Andújar, J. M., & Bravo, J. M. (2005). Multivariable fuzzy control applied to the physical-chemical
treatment facility of a cellulose factory. Fuzzy Sets and Systems , 150 (3), 475-492. doi: 10.1016/
j.fss.2004.03.023 .
Andújar, J. M., Aroba, J., Torre, M. L. d. l., & Grande, J. A. (2006). Contrast of evolution mod-
els for agricultural contaminants in ground waters by means of fuzzy logic and data mining.
Environmental Geology , 49 (3), 458-466. doi: 10.1007/s00254-005-0103-2 .
Andújar, J. M., Barragán, A. J., & Gegúndez, M. E. (2009). A general and formal methodology for
designing stable nonlinear fuzzy control systems. IEEE Transactions on Fuzzy Systems , 17 (5),
1081-1091. doi: 10.1109/TFUZZ.2009.2021984 .
Angelov, P., & Buswell, R. (2002). Identification of evolving fuzzy rule-based models. IEEE Trans-
actions on Fuzzy Systems , 10 (5), 667-677. doi: 10.1109/TFUZZ.2002.803499 .
Angelov, P. P., & Filev, D. P. (2004). An approach to online identification of Takagi-Sugeno fuzzy
models. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics , 34 (1), 484-
498. doi : 10.1109/TSMCB.2003.817053 .
Aroba, J., Grande, J. A., Andújar, J. M., & Riquelme, J. C. (2007). Application of fuzzy logic and
data mining techniques as tools for qualitative interpretation of acid mine drainage processes.
Environmental Geology , 53 (1), 135-145. doi: 10.1007/s00254-006-0627-0 .
Babuška, R. (1995). Fuzzy modeling—a control engineering perspective. Proceedings of FUZZ-
IEEE/IFES'95 , (Vol. 4, pp. 1897-1902) (Yokohama, Japan). doi: 10.1109/FUZZY.1995.409939 .
Babuška, R., Setnes, M., Kaymak, U., & van Nauta Lemke, H. R. (1996). Rule base simplification
with similarity measures. Proceedings of the 5th IEEE International Conference on Fuzzy Systems ,
(Vol. 3, pp. 1642-1647), (New Orleans, LA). doi: 10.1109/FUZZY.1996.552616 .
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