Reference
A. Núñez, B. De Schutter, D. Sáez, and I. Škrjanc,
"Hybrid-fuzzy modeling and identification,"
Applied Soft
Computing, vol. 17, pp. 67-78, Apr. 2014.
Abstract
In this paper a class of hybrid-fuzzy models is presented, where binary
membership functions are used to capture the hybrid behavior. We describe a
hybrid-fuzzy identification methodology for nonlinear hybrid systems with mixed
continuous and discrete states that uses fuzzy clustering and principal
component analysis. 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 (I/O) data. Next, using the detected switching points, a
hard partition of the I/O space is obtained. Finally, TS fuzzy models are
identified as submodels for each partition. Two illustrative examples, a
hybrid-tank system and a traffic model for highways, are presented to show the
benefits of the proposed approach.
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BibTeX
@article{NunDeS:14-009,
author = {N{\'{u}}{\~{n}}ez, Alfredo and De Schutter, Bart and S{\'{a}}ez,
Doris and {\v{S}}krjanc, Igor},
title = {Hybrid-Fuzzy Modeling and Identification},
journal = {Applied Soft Computing},
volume = {17},
pages = {67--78},
month = apr,
year = {2014}
}