Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Two approaches to data-driven design of evolving fuzzy systems: eTS and FLEXFIS
AU - Angelov, P.
AU - Lughofer, E.
AU - Klement, E. P.
PY - 2005/6/22
Y1 - 2005/6/22
N2 - In this paper two approaches for the incremental data-driven learning of one of the most effective fuzzy model, namely of so-called Takagi-Sugeno type, are compared. The algorithms that realise these approaches include not only adaptation of linear parameters in fuzzy systems appearing in the rule consequents, but also incremental learning and evolution of premise parameters appearing in the membership functions (i.e. fuzzy sets) in sample mode together with a rule learning strategy. In this sense the proposed methods are applicable for fast model training tasks in various industrial processes, whenever there is a demand of online system identification in order to apply models representing nonlinear system behaviors to system monitoring, online fault detection or open-loop control. An evaluation of the incremental learning algorithms are included at the end of the paper, where a comparison between conventional batch modelling methods for fuzzy systems and the incremental learning methods demonstrated in this paper is made with respect to model qualities and computation time. This evaluation is based on high dimensional data coming from an industrial measuring process as well as from a known source on the Internet, which underlines the usage of the new method for fast online identification tasks.
AB - In this paper two approaches for the incremental data-driven learning of one of the most effective fuzzy model, namely of so-called Takagi-Sugeno type, are compared. The algorithms that realise these approaches include not only adaptation of linear parameters in fuzzy systems appearing in the rule consequents, but also incremental learning and evolution of premise parameters appearing in the membership functions (i.e. fuzzy sets) in sample mode together with a rule learning strategy. In this sense the proposed methods are applicable for fast model training tasks in various industrial processes, whenever there is a demand of online system identification in order to apply models representing nonlinear system behaviors to system monitoring, online fault detection or open-loop control. An evaluation of the incremental learning algorithms are included at the end of the paper, where a comparison between conventional batch modelling methods for fuzzy systems and the incremental learning methods demonstrated in this paper is made with respect to model qualities and computation time. This evaluation is based on high dimensional data coming from an industrial measuring process as well as from a known source on the Internet, which underlines the usage of the new method for fast online identification tasks.
KW - Incremental learning
KW - adaptation of parameters
KW - evolving Takagi-Sugeno fuzzy systems
KW - online identification
KW - rule learning
U2 - 10.1109/NAFIPS.2005.1548502
DO - 10.1109/NAFIPS.2005.1548502
M3 - Conference contribution/Paper
SN - 0-7803-9187-X
SP - 31
EP - 35
BT - Fuzzy Information Processing Society, 2005. NAFIPS 2005. Annual Meeting of the North American
PB - IEEE
T2 - 2005 Annual Meeting of the North American Fuzzy Information Processing Society Annual Conference
Y2 - 21 June 2005 through 25 June 2005
ER -