n4sidOptions
Option set for n4sid
Syntax
opt = n4sidOptions
opt = n4sidOptions(Name,Value)
Description
creates
the default options set for opt
= n4sidOptionsn4sid
.
creates
an option set with the options specified by one or more opt
= n4sidOptions(Name,Value
)Name,Value
pair
arguments.
Input Arguments
Name-Value Arguments
Specify optional pairs of arguments as
Name1=Value1,...,NameN=ValueN
, where Name
is
the argument name and Value
is the corresponding value.
Name-value arguments must appear after other arguments, but the order of the
pairs does not matter.
Before R2021a, use commas to separate each name and value, and enclose
Name
in quotes.
InitialState
— Handling of initial states
'estimate'
(default) | 'zero'
Handling of initial states during estimation, specified as one of the following values:
'zero'
— The initial state is set to zero.'estimate'
— The initial state is treated as an independent estimation parameter.
N4Weight
— Weighting scheme used for singular-value decomposition by the N4SID
algorithm
'auto'
(default) | 'MOESP'
| 'CVA'
| 'SSARX'
Weighting scheme used for singular-value decomposition by the N4SID algorithm, specified as one of the following values:
'MOESP'
— Uses the MOESP algorithm by Verhaegen [2].'CVA'
— Uses the Canonical Variate Algorithm by Larimore [1].Estimation using frequency-domain data always uses
'CVA'
.'SSARX'
— A subspace identification method that uses an ARX estimation based algorithm to compute the weighting.Specifying this option allows unbiased estimates when using data that is collected in closed-loop operation. For more information about the algorithm, see [4].
'auto'
— The estimating function chooses between theMOESP
,CVA
andSSARX
algorithms.
N4Horizon
— Forward- and backward-prediction horizons used by the N4SID
algorithm
'auto'
(default) | vector [r sy su]
| k
-by-3 matrix
Forward- and backward-prediction horizons used by the N4SID algorithm, specified as one of the following values:
A row vector with three elements —
[r sy su]
, wherer
is the maximum forward prediction horizon, using up tor
step-ahead predictors.sy
is the number of past outputs, andsu
is the number of past inputs that are used for the predictions. See pages 209 and 210 in [3] 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 them. Making'N4Horizon'
ak
-by-3 matrix means that each row of'N4Horizon'
is tried, and the value that gives the best (prediction) fit to data is selected.k
is the number of guesses of[r sy su]
combinations. If you specify N4Horizon as a single column,r = sy = su
is used.'auto'
— The software uses an Akaike Information Criterion (AIC) for the selection ofsy
andsu
.
Focus
— Error to be minimized
'prediction'
(default) | 'simulation'
Error to be minimized in the loss function during estimation,
specified as the comma-separated pair consisting of 'Focus'
and
one of the following values:
'prediction'
— The one-step ahead prediction error between measured and predicted outputs is minimized during estimation. As a result, the estimation focuses on producing a good predictor model.'simulation'
— The simulation error between measured and simulated outputs is minimized during estimation. As a result, the estimation focuses on making a good fit for simulation of model response with the current inputs.
The Focus
option can be interpreted as a
weighting filter in the loss function. For more information, see Loss Function and Model Quality Metrics.
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.
Specify WeightingFilter
as one of the following
values:
[]
— No weighting prefilter is used.Passbands — 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,
[wl,wh]
wherewl
andwh
represent lower and upper limits of a passband. For a matrix with several rows defining frequency passbands,[w1l,w1h;w2l,w2h;w3l,w3h;...]
, the estimation algorithm uses the union of the frequency ranges to define the estimation passband.Passbands are expressed in
rad/TimeUnit
for time-domain data and inFrequencyUnit
for frequency-domain data, whereTimeUnit
andFrequencyUnit
are the time and frequency units of the estimation data.SISO filter — Specify a single-input-single-output (SISO) linear filter in one of the following ways:
A SISO LTI model
{A,B,C,D}
format, which specifies the state-space matrices of a filter with the same sample time as estimation data.{numerator,denominator}
format, which specifies the numerator and denominator of the filter as a transfer function with same sample time as estimation data.This option calculates the weighting function as a product of the filter and the input spectrum to estimate the transfer function.
Weighting vector — 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 data set,
Data.Frequency
. Each input and output response in the data is multiplied by the corresponding weight at that frequency.
EnforceStability
— Control whether to enforce stability of model
false
(default) | true
Control whether to enforce stability of estimated model, specified
as the comma-separated pair consisting of 'EnforceStability'
and
either true
or false
.
Data Types: logical
EstimateCovariance
— Control whether to generate parameter covariance data
true
(default) | false
Controls whether parameter covariance data is generated, specified as
true
or false
.
If EstimateCovariance
is true
, then use
getcov
to fetch the covariance matrix
from the estimated model.
Display
— Specify whether to display estimation progress
'off'
(default) | 'on'
Specify whether to display the estimation progress, specified as one of the following values:
'on'
— Information on model structure and estimation results are displayed in a progress-viewer window.'off'
— No progress or results information is displayed.
InputOffset
— Removal of offset from time-domain input data during estimation
[]
(default) | vector of positive integers | matrix
Removal of offset from time-domain input data during estimation, specified as one of the following:
A column vector of positive integers of length Nu, where Nu is the number of inputs.
[]
— Indicates no offset.Nu-by-Ne matrix — For multi-experiment data, specify
InputOffset
as an Nu-by-Ne matrix. Nu is the number of inputs and Ne is the number of experiments.
Each entry specified by InputOffset
is
subtracted from the corresponding input data.
OutputOffset
— Removal of offset from time-domain output data during estimation
[]
(default) | vector | matrix
Removal of offset from time-domain output data during estimation, specified as one of the following:
A column vector of length Ny, where Ny is the number of outputs.
[]
— Indicates no offset.Ny-by-Ne matrix — For multi-experiment data, specify
OutputOffset
as a Ny-by-Ne matrix. Ny is the number of outputs, and Ne is the number of experiments.
Each entry specified by OutputOffset
is
subtracted from the corresponding output data.
OutputWeight
— Weighting of prediction errors in multi-output estimations
[]
(default) | 'noise'
| positive semidefinite symmetric matrix
Weighting of prediction errors in multi-output estimations, specified as one of the following values:
'noise'
— Minimize , where E represents the prediction error andN
is the number of data samples. This choice is optimal in a statistical sense and leads to the maximum likelihood estimates in case no data is available about the variance of the noise. This option uses the inverse of the estimated noise variance as the weighting function.Positive semidefinite symmetric matrix (
W
) — Minimize the trace of the weighted prediction error matrixtrace(E'*E*W/N)
where:E
is the matrix of prediction errors, with one column for each output.W
is the positive semidefinite symmetric matrix of size equal to the number of outputs. UseW
to specify the relative importance of outputs in multiple-output models, or the reliability of corresponding data.N
is the number of data samples.
[]
— The software chooses between the'noise'
or using the identity matrix forW
.
This option is relevant only for multi-output models.
Advanced
— Additional advanced options
structure
Additional advanced options, specified as a structure with the
field MaxSize
. MaxSize
specifies
the maximum number of elements in a segment when input-output data
is split into segments.
MaxSize
must be a positive integer.
Default: 250000
Output Arguments
opt
— Option set for n4sid
n4sidOptions
option set
Option set for n4sid
,
returned as an n4sidOptions
option set.
Examples
Create Default Options Set for State-Space Estimation Using Subspace Method
opt = n4sidOptions;
Specify Options for State-Space Estimation Using Subspace Method
Create an options set for n4sid
using the 'zero'
option to initialize the state. Set the Display
to 'on'
.
opt = n4sidOptions('InitialState','zero','Display','on');
Alternatively, use dot notation to set the values of opt
.
opt = n4sidOptions; opt.InitialState = 'zero'; opt.Display = 'on';
References
[1] Larimore, W.E. “Canonical variate analysis in identification, filtering and adaptive control.” Proceedings of the 29th IEEE Conference on Decision and Control, pp. 596–604, 1990.
[2] Verhaegen, M. “Identification of the deterministic part of MIMO state space models.” Automatica, Vol. 30, 1994, pp. 61–74.
[3] Ljung, L. System Identification: Theory for the User. Upper Saddle River, NJ: Prentice-Hall PTR, 1999.
[4] Jansson, M. “Subspace identification and ARX modeling.” 13th IFAC Symposium on System Identification, Rotterdam, The Netherlands, 2003.
Version History
Introduced in R2012aR2018a: Renaming of Estimation and Analysis Options
The names of some estimation and analysis options were changed in R2018a. Prior names still work. For details, see the R2018a release note Renaming of Estimation and Analysis Options.
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