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g = spafdr(data) g = spafdr(data,Resol,w)
spafdr estimates the idfrd object
containing transfer function and the noise spectrum
of
the general linear model
![]()
where
is the spectrum of
.
data contains the output-input data as an iddata object. The data can be complex valued, and either time or frequency domain. It can also be an idfrd object containing frequency-response data.
g is returned as an idfrd object
(see idfrd)
with the estimate of
at the frequencies
specified by row
vector w. g also includes information
about the spectrum estimate of
at the same frequencies.
Both results are returned with estimated covariances, included in g.
See idfrd.
The normalization of the spectrum is the same as described under spa.
The frequency variable w is either specified as a row vector of frequencies, or as a cell array {wmin,wmax}. In the latter case the covered frequencies will be 50 logarithmically spaced points from wmin to wmax. You can change the number of points to NP by entering {wmin,wmax,NP}.
Omitting w or entering it as an empty matrix gives the default value, which is 100 logarithmically spaced frequencies between the smallest and largest frequency in data. For time-domain data, this means from 1/N*Ts to pi*Ts, where Ts is the sampling interval of data and N is the number of data.
The argument Resol defines the frequency resolution of the estimates. The resolution (measured in rad/s) is the size of the smallest detail in the frequency function and the spectrum that is resolved by the estimate. The resolution is a tradeoff between obtaining estimates with fine, reliable details, and suffering from spurious, random effects: The finer the resolution, the higher the variance in the estimate. Resol can be entered as a scalar (measured in rad/s), which defines the resolution over the whole frequency interval. It can also be entered as a row vector of the same length as w. Then Resol(k) is the local, frequency-dependent resolution around frequency w(k).
The default value of Resol, obtained by omitting it or entering it as the empty matrix, is Resol(k) = 2(w(k+1)-w(k)), adjusted upwards, so that a reasonable estimate is guaranteed. In all cases, the resolution is returned in the variable g.EstimationInfo.WindowSize.
If the data is given in the time domain, it is first converted to the frequency domain. Then averages of Y(w)Conj(U(w)) and U(w)Conj(U(w)) are formed over the frequency ranges w, corresponding to the desired resolution around the frequency in question. The ratio of these averages is then formed for the frequency-function estimate, and corresponding expressions define the noise spectrum estimate.
| bode | |
| etfe | |
| ffplot | |
| freqresp | |
| idfrd | |
| nyquist | |
| spa |
![]() | spa | ss | ![]() |

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