merge (iddata) (System Identification Toolbox)

Merge different data sets into one iddata object.

Syntax

```
dat = merge(dat1,dat2,....,datN)
```

Description

dat collects the data sets in dat1,.. datN into one iddata object, with several experiments. The number of experiments in dat will the sum of the number of experiments in datk. For the merging to be allowed a number of conditions must be satisfied:

All of datk must have the same number of input channels, and the InputNames must be the same.
All of datk must have the same number of output channels, and the OutputNames must be the same. If some input or output channel is lacking in one experiment, it can be replaced by a vector of NaN's to conform with these rules.
If the ExperimentNames of datk have been specified to something else than the default 'Exp1', 'Exp2', etc., they must all be unique. If default names overlap, they will be modified, so that dat will have a unique list of ExperimentNames.

The sampling intervals, the number of observations, and the input properties (Period, InterSample) may be different in the different experiments.

The individual experiments can be retrieved by the command getexp. The can also be retrieved by subreferencing with a fourth index:.

dat1 = dat(:,:,:,ExperimentNumber) or dat1 = dat(:,:,:,ExperimentName)

Storing multiple experiments as one iddata object may be very useful to handle experimental data that have been collected on different occasions, or when a data set has been split up to remove "bad" portions of the data. All the toolbox's routines accept multiple experiment data.

Example

Bad portions of data have been detected around sample 500 and between samples 720 - 730. Cut out these bad portions and form a multiple experiment data set that can be used to estimate models, without the bad data destroying the estimate.

dat = merge(dat(1:498),dat(502:719),dat(719:1000))
m = pem(dat)

Use the first two parts to estimate the model and the third one for validation.

```
m = pem(dat{1:2});
compare(dat{3},m)
```