Reference:
A. Núñez,
B. De Schutter,
D. Sáez, and
I. Skrjanc,
"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.