Reference
A. Núñez, D. Sáez, I. Škrjanc, and B. De Schutter,
"A new method for hybrid-fuzzy identification,"
Proceedings of
the 18th IFAC World Congress, Milan, Italy, pp. 15013-15018, Aug.-Sept.
2011.
Abstract
In this paper a new identification method for non-linear hybrid systems that
have mixed continuous and discrete states by using fuzzy clustering and
principal component analysis is described. The method first determines the
hybrid characteristic of the system inspired by an inverse form of the merge
method for clusters, which makes it possible to identify the unknown switching
points of a process based on just input-output data. Using the switching
points, a hard partition of the input-output space is obtained. Then, we
propose to use Takagi-Sugeno (TS) fuzzy models with Gaussian MFs as sub-models
for each partition. Thus, the overall model is hybrid-fuzzy and will include
explicitly the hybrid behavior of the system (the detected switching points) by
means of binary MFs, and in each partition all the other non-linearities by
means of TS sub-models. An illustrative experiment on a hybrid-tank system is
conducted to present the benefits of the proposed approach.
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BibTeX
@inproceedings{NunSae:11-009,
author = {N{\'{u}}{\~{n}}ez, Alfredo and S{\'{a}}ez, Doris and
{\v{S}}krjanc, Igor and De Schutter, Bart},
title = {A New Method for Hybrid-Fuzzy Identification},
booktitle = {Proceedings of the 18th IFAC World Congress},
address = {Milan, Italy},
pages = {15013--15018},
month = aug # {--} # sep,
year = {2011}
}