Application of the Extended Kalman filter to fuzzy modeling: Algorithms and practical implementation

DOI

10.2991/eusflat.2011.23How to use a DOI?

Keywords

Algorithm, Kalman filter, estimation, fuzzy system, modeling.

Abstract

Modeling phase is fundamental both in the analysis process of a dynamic system and the design of a control system. If this phase is in-line is even more critical and the only information of the system comes from input/output data. Some adaptation algorithms for fuzzy system based on extended Kalman filter are presented in this paper, which allows obtaining accurate models without renounce the computational efficiency that characterizes the Kalman filter, and allows its implementation in-line with the process.

Copyright

© 2011, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Antonio Javier Barragán Piña
AU  - José Manuel Andújar Márquez
AU  - Mariano J. Aznar Torres
AU  - Agustín Jiménez
AU  - Basil M Al-Hadithi
PY  - 2011/08
DA  - 2011/08
TI  - Application of the Extended Kalman filter to fuzzy modeling: Algorithms and practical implementation
BT  - Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-11)
PB  - Atlantis Press
SP  - 691
EP  - 698
SN  - 1951-6851
UR  - https://doi.org/10.2991/eusflat.2011.23
DO  - 10.2991/eusflat.2011.23
ID  - Piña2011/08
ER  -