Merge data sets into iddata object
dat = merge(dat1,dat2,....,datN)
dat collects the data sets in
...,datN into one
iddata object, with
several experiments. The number of experiments
dat will be the sum of the number of experiments
datk. For the merging to be allowed, a number
of conditions must be satisfied:
datkmust have the same number of input channels, and the
InputNamesmust be the same.
datkmust have the same number of output channels, and the
OutputNamesmust be the same. If some input or output channel is lacking in one experiment, it can be replaced by a vector of
NaNs to conform with these rules.
datkhave been specified as something other than the default
'Exp2', etc., they must all be unique. If default names overlap, they are modified so that
datwill have a list of unique
The sampling intervals, the number of observations, and the
input properties (
might be different in the different experiments.
You can retrieve the individual experiments by using the command
You can also retrieve them by subreferencing with a fourth index.
dat1 = dat(:,:,:,ExperimentNumber)
dat1 = dat(:,:,:,ExperimentName)
Storing multiple experiments as one
can be very useful for handling experimental data that has been collected
on different occasions, or when a data set has been split up to remove
“bad” portions of the data. All the toolbox routines
accept multiple-experiment data.
Merge Multiple Data Sets
Remove bad portions of data to estimate models without the bad data destroying the estimate.
load iddemo8; plot(dat);
Bad portions of data are detected around sample 250 to 280 and between samples 600 to 650. Cut out these bad portions to form a multiple-experiment data set and merge the data.
dat = merge(dat(1:250),dat(281:600),dat(651:1000));
You can use the first two experiments to estimate a model and the third experiment to validate the model.
dat_est = getexp(dat,[1,2]); m = ssest(dat_est,2); dat_val = getexp(dat,3);
Introduced before R2006a