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A general methodology for online TS fuzzy modeling by the extended Kalman filter

AuthorsBarragán, Antonio Javier; Al-Hadithi, Basil Mohammed ; Jiménez, Agustín ; Andújar, José Manuel
Kalman filter
Fuzzy system
Issue Date2014
CitationApplied Soft Computing Journal 18: 277- 289 (2014)
AbstractThis paper presents an online TS fuzzy modeling general methodology based on the extended Kalman filter. The model can be obtained in a recursive way only based on input-output data. The methodology can work online with the system, properly in the presence of noise, is very efficient computationally and completely general. It is general in the sense theorically there are no restrictions neither in the number of inputs nor outputs, neither in the type nor distribution of membership functions used (which can even be mixed in the antecedents of the rules). Some examples and comparisons with other online fuzzy identification models from signals are provided to illustrate the skill of the online identification of the proposed methodology. © 2013 Elsevier B.V. All rights reserved.
Publisher version (URL)https://doi.org/10.1016/j.asoc.2013.09.005
Appears in Collections:(CAR) Artículos
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