Reference:
A. Núñez,
D. Sáez,
I. Skrjanc, 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.