Option set for
opt = greyestOptions
opt = greyestOptions(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
InitialState— Handling of initial states
Handling of initial states during estimation, specified as one of the following values:
'model' — The initial state
is parameterized by the ODE file used by the
The ODE file must return 6 or more output arguments.
'zero' — The initial state
is set to zero. Any values returned by the ODE file are ignored.
'estimate' — The initial
state is treated as an independent estimation parameter.
'backcast' — The initial
state is estimated using the best least squares fit.
'auto' — The software chooses
the method to handle initial states based on the estimation data.
Vector of doubles — Specify a column vector of length Nx, where Nx is the number of states. For multiexperiment data, specify a matrix with Ne columns, where Ne is the number of experiments. The specified values are treated as fixed values during the estimation process.
DisturbanceModel— Handling of disturbance component
Handling of the disturbance component (K) during estimation, specified as one of the following values:
'model' — K values
are parameterized by the ODE file used by the
The ODE file must return 5 or more output arguments.
'fixed' — The value of the
idgrey model is fixed to its original
'none' — K is
fixed to zero. Any values returned by the ODE file are ignored.
'estimate' — K is
treated as an independent estimation parameter.
'auto' — The software chooses
the method to handle how the disturbance component is handled during
estimation. The software uses the
if the ODE file returns 5 or more output arguments with a finite value
for K. Else, the software uses the
Noise model cannot be estimated using frequency domain data.
Advanced— Additional advanced options
Additional advanced options, specified as a structure with the following fields:
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 state.
The initial state 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 = greyestOptions;
Create an options set for
greyest using the
'backcast' algorithm to initialize the state. Specify
opt = greyestOptions('InitialState','backcast','Display','on');
Alternatively, use dot notation to set the values of
opt = greyestOptions; opt.InitialState = 'backcast'; opt.Display = 'on';
 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.
 Ljung, L. System Identification: Theory for the User. Upper Saddle River, NJ: Prentice-Hall PTR, 1999.