njit-etd2003-081 - New Jersey Institute of Technology

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ysim = idsim( rp , th );
plot([ hr (40000:50000) ysim(40000:50000)])
Note that the function compare does this sequence of commands more efficiently.
One can see that the model is quite capable of describing the system, even for data that
were not used in calculating the fit.
9. To compute and graph the poles and zeros of the ARX model, use
zpth = th2zp(th);
zpplot(zp th);
10. If one wants to know the frequency response, one can compute the frequency
function of the model and present it as a Bode plot by entering
gth = th2ff(th);
bodeplot(gth);
11. Compare this transfer function with a transfer function obtained from a
nonparametric, spectral analysis method. Such an estimate is obtained directly from the
data
gs = spa(z2);
gs = sett(gs,0.005);
The sampling interval, 0.005, is set by the second command in order to obtain
correct frequency scales. The function spa also allows you to select window sizes,
frequency ranges, etc. All these values have been given as default values.
12. One can then compare the estimate of the transfer function obtained by spectral
analysis versus the one obtained from the model (B.1) with
bodeplot([gs gth]);