You might be interested in only a specific frequency range of the frequency response for a model, in this case, you can filter and enhance the data in the frequency range to improve the fit in the regions of interest.

If the sampling frequency is much higher than the bandwidth of the system, the sampling frequency might substantially increase the computation burden for complicated identification algorithms. You can decrease the sampling frequency by taking every nth sample to construct a new downsampled data set. Applying an anti-alias filter on the data before downsampling prevents corruption of the downsampled data set.

You can use the SI Lowpass Filter VI or the SI Bandpass Filter VI to apply a lowpass or bandpass filter, respectively, to the data from the system. You then can use the SI Down Sampling VI to reduce the number of samples in the data set.

After preprocessing the data you acquired from a dynamic system, the result is a data set that you can use to estimate a model that reflects the system dynamics.