Option set for
tfestOptions object to specify options for estimating
transfer function models using the
tfest function. You can specify options such as
the estimation objective, the handling of initial conditions, and the numerical search method
to be used in estimation.
creates the default
option set for estimating a transfer function model using
opt = tfestOptions
tfest. To modify the properties of this option set for your specific
application, use dot notation.
creates an option set with properties specified using one or more name-value
opt = tfestOptions(
InitializeMethod — Algorithm used to initialize numerator and denominator
'iv' (default) |
Algorithm used to initialize the values of the numerator and denominator of the
tfest, specified as one of the following values:
'iv'— Instrument Variable approach.
'svf'— State Variable Filters approach.
'gpmf'— Generalized Poisson Moment Functions approach.
'n4sid'— Subspace state-space estimation approach.
'all'— Combination of all of the preceding approaches. The software tries all these methods and selects the method that yields the smallest value of the prediction error norm.
This property is applicable only for estimation of continuous-time transfer functions using time-domain data
InitializeOptions — Option set for initialization algorithm
Option set for the initialization algorithm used to initialize the values of the
numerator and denominator of the output of
tfest, specified as a
structure with the fields in the following table.
Calculates the weighting matrices used in the singular-value
decomposition step of the
Determines the forward and backward prediction horizons used by the
See pages 209 and 210 in  for more information. These numbers can have a substantial influence on the
quality of the resulting model, and there are no simple rules for choosing
Time constant of the differentiating filter used by the
Ts is the sample time of the estimation data.
|Maximum number of iterations. Applicable when
|Convergence tolerance. Applicable when
InitialCondition — Handling of initial conditions
'auto' (default) |
Handling of initial conditions during estimation, specified as one of the following values:
'zero'— All initial conditions are taken as zero.
'estimate'— The necessary initial conditions are treated as estimation parameters.
'backcast'— The necessary initial conditions are estimated by a backcasting (backward filtering) process, described in .
'auto'— An automatic choice among the preceding options is made, guided by the data.
WeightingFilter — Weighting prefilter
 (default) | vector | matrix | cell array | linear system |
Weighting prefilter applied to the loss function to be minimized during estimation.
To understand the effect of
WeightingFilter on the loss function, see
Loss Function and Model Quality Metrics.
WeightingFilter as one of the values in the following
|No weighting prefilter is used.|
Specify a row vector or matrix containing frequency values that
define desired passbands. You select a frequency band where the fit between
estimated model and estimation data is optimized. For example, specify
Passbands are expressed in
Specify a single-input-single-output (SISO) linear filter in one of the following ways:
Applicable for frequency-domain data only. Specify a column vector of
weights. This vector must have the same length as the frequency vector of the
Applicable for estimation using frequency-response data only. Use as the weighting filter, where G(ω) is the complex frequency-response data. Use this option for capturing relatively low amplitude dynamics in data, or for fitting data with high modal density. This option also makes it easier to specify channel-dependent weighting filters for MIMO frequency-response data.
Applicable for estimation using frequency-response data only. Use as the weighting filter. Use this option for capturing relatively low amplitude dynamics in data, or for fitting data with high modal density. This option also makes it easier to specify channel-dependent weighting filters for MIMO frequency-response data.
InputInterSample — Input-channel intersample behavior
Input-channel intersample behavior for transformations between discrete time and continuous time, specified as
The definitions of the three behavior values are as follows:
'zoh'— Zero-order hold maintains a piecewise-constant input signal between samples.
'foh'— First-order hold maintains a piecewise-linear input signal between samples.
'bl'— Band-limited behavior specifies that the continuous-time input signal has zero power above the Nyquist frequency.
iddata objects have a similar property,
data.InterSample, that contains the same behavior value options.
InputInterSample value is
the estimation data is in an
software uses the
data.InterSample value. When the estimation data
is instead contained in a timetable or a matrix pair, with the
option, the software uses
The software applies the same option value to all channels and all experiments.
Advanced — Additional advanced options
Additional advanced options, specified as a structure with the fields in the following table.
Error threshold at which to adjust the weight of large errors from quadratic to linear.
Errors larger than
Maximum number of elements in a segment when input-output data is split into segments.
Threshold for stability tests.
Threshold at which to automatically estimate initial conditions.
The software estimates the initial conditions when:
Create Default Options Set for Transfer Function Estimation
opt = tfestOptions;
Specify Options for Transfer Function Estimation
Create an options set for
tfest using the
'n4sid' initialization algorithm and set the
opt = tfestOptions('InitializeMethod','n4sid','Display','on');
Alternatively, use dot notation to set the values of
opt = tfestOptions; opt.InitializeMethod = 'n4sid'; opt.Display = 'on';
 Ljung, Lennart. System Identification: Theory for the User. 2nd Ed. Upper Saddle River, NJ: Prentice-Hall PTR, 1999.
 Knudsen, T. "A New method for estimating ARMAX models," IFAC Proceedings Volumes 27, no. 8 (July 1994): 895–901. https://doi.org/10.1016/S1474-6670(17)47823-2.
 Wills, Adrian, B. Ninness, and S. Gibson. “On Gradient-Based Search for Multivariable System Estimates.” IFAC Proceedings Volumes 38, No 1 (2005): 832–837. https://doi.org/10.3182/20050703-6-CZ-1902.00140.
 Garnier, H., M. Mensler, and A. Richard. “Continuous-time Model Identification From Sampled Data: Implementation Issues and Performance Evaluation” International Journal of Control 76, no 13 (January 2003): 1337–57. https://doi.org/10.1080/0020717031000149636.
 Ljung, Lennart. “Experiments With Identification of Continuous-Time Models.” IFAC Proceedings Volumes 42, no. 10 (2009):1175–80. https://doi.org/10.3182/20090706-3-FR-2004.00195.
 Jansson, Magnus. “Subspace identification and ARX modeling.” IFAC Proceedings Volumes 36 no. 16 (September 2003): 1585–90. https://doi.org/10.1016/S1474-6670(17)34986-8
Version HistoryIntroduced in R2012b
InputInterSample option allows intersample behavior specification for continuous models estimated from timetables or matrices.
iddata objects contain an
InterSample property that
describes the behavior of the signal between sample points. The
InputInterSample option implements a version of that property in
tfestOptions so that intersample behavior can be specified also when
estimation data is stored in timetables or matrices.