Options set for
opt = procestOptions
opt = procestOptions(Name,Value)
comma-separated pairs of
the argument name and
Value is the corresponding value.
Name must appear inside quotes. You can specify several name and value
pair arguments in any order as
'InitialCondition'— Handling of initial conditions
Handling of initial conditions during estimation, specified as one of the following values:
'zero' — The initial condition
is set to zero.
'estimate' — The initial
condition is treated as an independent estimation parameter.
'backcast' — The initial
condition is estimated using the best least squares fit.
'auto' — The software chooses
the method to handle initial condition based on the estimation data.
'DisturbanceModel'— Handling of additive noise
Handling of additive noise (H) during estimation for the model
e is white noise, u is the input and y is the output.
H(s) is stored in the
of the numerator and denominator of
DisturbanceModel is specified as one of
the following values:
'none' — H is
fixed to one.
'estimate' — H is
treated as an estimation parameter. The software uses the value of
NoiseTF property as the initial guess.
'ARMA1' — The software estimates H as
a first-order ARMA model
'ARMA2' — The software estimates H as
a second-order ARMA model
'fixed' — The software fixes
the value of the
NoiseTF property of the
as the value of H.
A noise model cannot be estimated using frequency domain data.
'InputOffset'— Removal of offset from time-domain input data
'estimate'| vector | matrix | object |
Removal of offset from time-domain input data during estimation, specified as one of the following values:
'estimate' — The software
treats the input offsets as an estimation parameter.
'auto' — The software chooses
the method to handle input offsets based on the estimation data and
the model structure. The estimation either assumes zero input offset
or estimates the input offset.
For example, the software estimates the input offset for a model that contains an integrator.
A column vector of length Nu, where Nu is the number of inputs.
 to specify no offsets.
In case of multi-experiment data, specify
a Nu-by-Ne matrix. Nu is
the number of inputs, and Ne is the number of experiments.
Each entry specified by
subtracted from the corresponding input data.
A parameter object, constructed using
that imposes constraints on how the software estimates the input offset.
For example, create a parameter object for a 2-input model estimation. Specify the first input offset as fixed to zero and the second input offset as an estimation parameter.
opt = procestOptions; u0 = param.Continuous('u0',[0;NaN]); u0.Free(1) = false; opt.Inputoffset = u0;
'Advanced'— Additional advanced options
Advanced is a structure with the following
ErrorThreshold — Specifies
when to adjust the weight of large errors from quadratic to linear.
Errors larger than
ErrorThreshold times the
estimated standard deviation have a linear weight in the loss function.
The standard deviation is estimated robustly as the median of the
absolute deviations from the median of the prediction errors, divided
0.7. For more information on robust norm choices,
see section 15.2 of .
ErrorThreshold = 0 disables
robustification and leads to a purely quadratic loss function. When
estimating with frequency-domain data, the software sets
zero. For time-domain data that contains outliers, try setting
MaxSize — Specifies the
maximum number of elements in a segment when input-output data is
split into segments.
MaxSize must be a positive integer.
StabilityThreshold — Specifies
thresholds for stability tests.
StabilityThreshold is a structure with the
s — Specifies the location
of the right-most pole to test the stability of continuous-time models.
A model is considered stable when its right-most pole is to the left
z — Specifies the maximum
distance of all poles from the origin to test stability of discrete-time
models. A model is considered stable if all poles are within the distance
AutoInitThreshold — Specifies
when to automatically estimate the initial condition.
The initial condition is estimated when
ymeas is the measured output.
yp,z is the predicted output of a model estimated using zero initial states.
yp,e is the predicted output of a model estimated using estimated initial states.
opt = procestOptions;
Create an option set for
'simulation' and turning on the
opt = procestOptions('Focus','simulation','Display','on');
Alternatively, use dot notation to set the values of
opt = procestOptions; opt.Focus = 'simulation'; opt.Display = 'on';
 Ljung, L. System Identification: Theory for the User. Upper Saddle River, NJ: Prentice-Hall PTR, 1999.
 Wills, Adrian, B. Ninness, and S. Gibson. “On Gradient-Based Search for Multivariable System Estimates”. Proceedings of the 16th IFAC World Congress, Prague, Czech Republic, July 3–8, 2005. Oxford, UK: Elsevier Ltd., 2005.