oe
Estimate outputerror polynomial model using timedomain or frequencydomain data
Syntax
Description
Outputerror (OE) models are a special configuration of polynomial models, having
only two active polynomials—B and F. OE models represent
conventional transfer functions that relate measured inputs to outputs while also including
white noise as an additive output disturbance. You can estimate OE models using time and
frequencydomain data. The tfest
command offers the same functionality as
oe
. For tfest
, you specify the model orders using
number of poles and zeros rather than polynomial degrees. For continuoustime estimation,
tfest
provides faster and more accurate results, and is
recommended.
Estimate OE Model
estimates an OE model sys
= oe(tt
,[nb
nf nk]
)sys
using the data contained in the variables
of timetable tt
. The software uses the first Nu
variables as inputs and the next Ny variables as outputs, where
Nu and Ny are determined from the dimensions of
the specified polynomial orders.
sys
is represented by the equation
$$y(t)=\frac{B(q)}{F(q)}u(tnk)+e(t)$$
Here, y(t) is the output, u(t) is the input, and e(t) is the error.
The orders [nb nf nk]
define the number of parameters in each
component of the estimated polynomial.
To select specific input and output channels from tt
, use
namevalue syntax to set 'InputName'
and
'OutputName'
to the corresponding timetable variable names.
uses the timedomain input and output signals in the commaseparated matrices
sys
= oe(u
,y
,[nb nf nk]
)u
,y
. The software assumes that the data sample
time is 1 second. To change the sample time, set Ts
using namevalue
syntax.
uses the timedomain or frequencydomain data in the data object
sys
= oe(data
,[nb
nf nk]
)data
.
specifies model structure attributes using additional options specified by one or more
namevalue pair arguments. You can use this syntax with any of the previous inputargument
combinations.sys
= oe(___,Name,Value
)
Configure Initial Parameters
Specify Additional Estimation Options
Return Estimated Initial Conditions
[
returns the estimated initial conditions as an sys
,ic
] = oe(___)initialCondition
object. Use this syntax if you plan to simulate or predict the model response using the
same estimation input data and then compare the response with the same estimation output
data. Incorporating the initial conditions yields a better match during the first part of
the simulation.
Examples
Estimate OE Polynomial Model
Estimate an OE polynomial from timedomain data using two methods to specify input delay.
Load the estimation data.
load sdata1 tt1
Set the orders of the B and F polynomials nb
and nf
. Set the input delay nk
to one sample. Compute the model sys
.
nb = 2; nf = 2; nk = 1; sys = oe(tt1,[nb nf nk]);
Compare the simulated model response with the measured output.
compare(tt1,sys)
The plot shows that the fit percentage between the simulated model and the estimation data is greater than 70%.
Instead of using nk
, you can also use the namevalue pair argument 'InputDelay'
to specify the onesample delay.
nk = 0;
sys1 = oe(tt1,[nb nf nk],'InputDelay',1);
figure
compare(tt1,sys1)
The results are identical.
You can view more information about the estimation by exploring the idpoly
property sys.Report
.
sys.Report
ans = Status: 'Estimated using OE' Method: 'OE' InitialCondition: 'zero' Fit: [1x1 struct] Parameters: [1x1 struct] OptionsUsed: [1x1 idoptions.polyest] RandState: [1x1 struct] DataUsed: [1x1 struct] Termination: [1x1 struct]
For example, find out more information about the termination conditions.
sys.Report.Termination
ans = struct with fields:
WhyStop: 'Near (local) minimum, (norm(g) < tol).'
Iterations: 3
FirstOrderOptimality: 0.0708
FcnCount: 7
UpdateNorm: 1.4809e05
LastImprovement: 5.1744e06
The report includes information on the number of iterations and the reason the estimation stopped iterating.
Estimate ContinuousTime OE Model Using Frequency Response
Load the estimation data.
load oe_data1 data;
The idfrd
object data
contains the continuoustime frequency response for the following model:
$$G(s)=\frac{s+3}{{s}^{3}+2{s}^{2}+s+1}$$
Estimate the model.
nb = 2; nf = 3; sys = oe(data,[nb nf]);
Evaluate the goodness of fit.
compare(data,sys);
Estimate OE Model Using Regularization
Estimate a highorder OE model from data collected by simulating a highorder system. Determine the regularization constants by trial and error and use the values for model estimation.
Load the data.
load regularizationExampleData.mat m0simdata
Estimate an unregularized OE model of order 30.
m1 = oe(m0simdata,[30 30 1]);
Obtain a regularized OE model by determining the Lambda value using trial and error.
opt = oeOptions; opt.Regularization.Lambda = 1; m2 = oe(m0simdata,[30 30 1],opt);
Compare the model outputs with the estimation data.
opt = compareOptions('InitialCondition','z'); compare(m0simdata,m1,m2,opt);
The regularized model m2
produces a better fit than the unregularized model m1
.
Compare the variance in the model responses.
h = bodeplot(m1,m2); opt = getoptions(h); opt.PhaseMatching = 'on'; opt.ConfidenceRegionNumberSD = 3; opt.PhaseMatching = 'on'; setoptions(h,opt); showConfidence(h);
The regularized model m2
has a reduced variance compared to the unregularized model m1
.
Estimate Continuous Model Using BandLimited DiscreteTime FrequencyDomain Data
Load the estimation data data
and sample time Ts
.
load oe_data2.mat data Ts
An iddata
object data
contains the discretetime frequency response for the following model:
$$G(s)=\frac{1000}{s+500}$$
View the estimation sample time Ts
that you loaded.
Ts
Ts = 1.0000e03
This value matches the property data.Ts
.
data.Ts
ans = 1.0000e03
You can estimate a continuous model from data
by limiting the input and output frequency bands to the Nyquist frequency. To do so, specify the estimation prefilter option 'WeightingFilter
' to define a passband from 0
to 0.5*pi/Ts
rad/s. The software ignores any response values with frequencies outside of that passband.
opt = oeOptions('WeightingFilter',[0 0.5*pi/Ts]);
Set the Ts
property to 0
to treat data
as continuoustime data.
data.Ts = 0;
Estimate the continuous model.
nb = 1; nf = 3; sys = oe(data,[nb nf],opt);
Obtain Initial Conditions
Load the data, which consists of input and output data in matrix form and the sample time.
load sdata1i umat1i ymat1i Ts1i
Estimate an OE polynomial model sys
and return the initial conditions in ic
.
nb = 2;
nf = 2;
nk = 1;
[sys,ic] = oe(umat1i,ymat1i,[nb,nf,nk],'Ts',Ts1i);
ic
ic = initialCondition with properties: A: [2x2 double] X0: [2x1 double] C: [0.9428 0.4824] Ts: 0.1000
ic
is an initialCondition
object that encapsulates the free response of sys
, in statespace form, to the initial state vector in X0
. You can incorporate ic
when you simulate sys
with the umat1i
input signal and compare the response with the ymat1i
output signal.
Input Arguments
tt
— Timetablebased estimation data
timetable  cell array of timetables.
Estimation data, specified as a timetable
that uses a regularly spaced time
vector. tt
contains variables representing input and output
channels. For multiexperiment data, tt
is a cell array of
timetables of length Ne
, where Ne
is the
number of experiments
The software determines the number of input and output channels to use for estimation from the
dimensions of the specified polynomial orders. The input/output channel selection
depends on whether the 'InputName'
and
'OutputName'
namevalue arguments are specified.
If
'InputName'
and'OutputName'
are not specified, then the software uses the first Nu variables oftt
as inputs and the next Ny variables oftt
as outputs.If
'InputName'
and'OutputName'
are specified, then the software uses the specified variables. The number of specified input and output names must be consistent with Nu and Ny.For functions that can estimate a time series model, where there are no inputs,
'InputName'
does not need to be specified.
For more information about working with estimation data types, see Data Domains and Data Types in System Identification Toolbox.
u
, y
— Matrixbased estimation data
matrices  cell array of matrices
Estimation data, specified for SISO systems as a commaseparated pair of N_{s}by1 realvalued matrices that contain uniformly sampled input and output timedomain signal values. Here, N_{s} is the number of samples.
For MIMO systems, specify u
,y
as an
input/output matrix pair with the following dimensions:
u
— N_{s}byN_{u}, where N_{u} is the number of inputs.y
— N_{s}byN_{y}, where N_{y} is the number of outputs.
For multiexperiment data, specify u
,y
as a
pair of 1byN_{e} cell arrays, where
N_{e} is the number of experiments. The
sample times of all the experiments must match.
For time series data, which contains only outputs and no inputs, specify
[]
,y
.
Limitations
For more information about working with estimation data types, see Data Domains and Data Types in System Identification Toolbox.
data
— Estimation data
iddata
object  frd
object  idfrd
object
Estimation data, specified as an iddata
object, an frd
object, or an idfrd
object.
For timedomain estimation, data
must be an iddata
object containing the input and output signal values.
For frequencydomain estimation, data
can be one of the following:
Timedomain estimation data must be uniformly sampled. By default, the software sets the sample time of the model to the sample time of the estimation data.
For multiexperiment data, the sample times and intersample behavior of all the experiments must match.
You can compute discretetime models from timedomain data or discretetime
frequencydomain data. Use tfest
to compute continuoustime
models.
[nb nf nk]
— OE model orders
integer row vector  row vector of integer matrices
OE model orders, specified as a 1by3 vector or a vector of integer matrices.
For a system represented by
$$y(t)=\frac{B(q)}{F(q)}u(tnk)+e(t)$$
where y(t) is the output,
u(t) is the input, and
e(t) is the error, the elements of [nb
nf nk]
are as follows:
nb
— Order of the B(q) polynomial + 1, which is equivalent to the length of the B(q) polynomial.nb
is an N_{y}byN_{u} matrix. N_{y} is the number of outputs and N_{u} is the number of inputs.nf
— Order of the F polynomial.nf
is an N_{y}byN_{u} matrix.nk
— Input delay, expressed as the number of samples.nk
is an N_{y}byN_{u} matrix. The delay appears as leading zeros of the B polynomial.
For estimation using continuoustime frequencydomain data, specify only
[nb nf]
and omit nk
. For an example, see Estimate ContinuousTime OE Model Using Frequency Response.
init_sys
— Linear system
idpoly
model  linear model  structure
Linear system that configures the initial parameterization of
sys
, specified as an idpoly
model, another
linear model, or a structure. You obtain init_sys
either by
performing an estimation using measured data or by direct construction.
If init_sys
is an idpoly
model of the OE
structure, oe
uses the parameter values of
init_sys
as the initial guess for estimating
sys
. The sample time of init_sys
must match
the sample time of the data.
Use the Structure
property of init_sys
to
configure initial guesses and constraints for B(q)
and F(q). For example:
To specify an initial guess for the F(q) term of
init_sys
, setinit_sys.Structure.F.Value
as the initial guess.To specify constraints for the B(q) term of
init_sys
:Set
init_sys.Structure.B.Minimum
to the minimum B(q) coefficient values.Set
init_sys.Structure.B.Maximum
to the maximum B(q) coefficient values.Set
init_sys.Structure.B.Free
to indicate which B(q) coefficients are free for estimation.
If init_sys
is not a polynomial model of the OE structure, the
software first converts init_sys
to an OE structure model.
oe
uses the parameters of the resulting model as the initial
guess for estimating
sys
.
If you do not specify opt
and init_sys
was
obtained by estimation, then the software uses estimation options from
init_sys.Report.OptionsUsed
.
opt
— Estimation options
oeOptions
option set
Estimation options, specified as an oeOptions
option set. Options specified by opt
include:
Estimation objective
Handling of initial conditions
Numerical search method and the associated options
For examples of specifying estimation options, see Estimate Continuous Model Using BandLimited DiscreteTime FrequencyDomain Data.
NameValue Arguments
Specify optional pairs of arguments as
Name1=Value1,...,NameN=ValueN
, where Name
is
the argument name and Value
is the corresponding value.
Namevalue 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.
Example: 'InputDelay',1
InputName
— Input channel names
string  character vector  string array  cell array of character vectors
Input channel names, specified as a string, character vector, string array, or cell array of character vectors.
If you are using a timetable for the data source, the names in
InputName
must be a subset of the timetable variables.
Example: sys = oe(tt,__,'InputName',["u1" "u2"])
selects
the variables u1
and u2
as the input channels from
the timetable tt
to use for the estimation.
OutputName
— Output channel names
string  character vector  string array  cell array of character vectors
Output channel names, specified as a string, character vector, string array, or cell array of character vectors.
If you are using a timetable for the data source, the names in
OutputName
must be a subset of the timetable variables.
Example: sys = oe(tt,__,'OutputName',["y1" "y3"])
selects
the variables y1
and y3
as the output channels
from the timetable tt
to use for the estimation.
Ts
— Sample time
1
(default)  positive scalar
Sample time, specified as the commaseparated pair consisting of 'Ts'
and
the sample time in the units specified by TimeUnit
. When you use
matrixbased data (u
,y
), you must specify
Ts
if you require a sample time other than the assumed sample
time of 1 second.
To obtain the data sample time for a timetable tt
, use the timetable property tt.Properties.Timestep
.
Example: oe(umat1,ymat1,___,'Ts',0.08)
computes a model with sample
time of 0.08 seconds.
InputDelay
— Input delays
0 (default)  positive integer vector  integer scalar
Input delays for each input channel, specified as the commaseparated pair
consisting of 'InputDelay'
and a numeric vector.
For continuoustime models, specify
'InputDelay'
in the time units stored in theTimeUnit
property.For discretetime models, specify
'InputDelay'
in integer multiples of the sample timeTs
. For example, setting'InputDelay'
to3
specifies a delay of three sampling periods.
For a system with N_{u} inputs, set
InputDelay
to an
N_{u}by1 vector. Each entry of this vector is
a numerical value that represents the input delay for the corresponding input
channel.
To apply the same delay to all channels, specify 'InputDelay'
as a scalar.
For an example, see Estimate OE Polynomial Model.
IODelay
— Transport delays
0 (default)  scalar  numeric array
Transport delays for each inputoutput pair, specified as the commaseparated pair
consisting of 'IODelay'
and a numeric array.
For continuoustime models, specify
'IODelay'
in the time units stored in theTimeUnit
property.For discretetime models, specify
'IODelay'
in integer multiples of the sample timeTs
. For example, setting'IODelay'
to4
specifies a transport delay of four sampling periods.
For a system with N_{u} inputs and
N_{y} outputs, set
'IODelay'
to an
N_{y}byN_{u}
matrix. Each entry is an integer value representing the transport delay for the
corresponding inputoutput pair.
To apply the same delay to all channels, specify 'IODelay'
as
a scalar.
You can specify 'IODelay'
as an alternative to the
nk
value. Doing so simplifies the model structure by reducing the
number of leading zeros in the B polynomial. In particular, you can
represent max(nk1,0)
leading zeros as inputoutput delays using
'IODelay'
instead.
Output Arguments
sys
— OE polynomial model
idpoly
object
OE polynomial model that fits the estimation data, returned as an idpoly
model object. This model is created using the specified model
orders, delays, and estimation options. The sample time of sys
matches the sample time of the estimation data. Therefore, sys
is
always a discretetime model when estimated from timedomain data. For continuoustime
model identification using timedomain data, use tfest
.
The Report
property of the model stores information about the
estimation results and options used. Report
has the following
fields.
Report Field  Description 

Status  Summary of the model status, which indicates whether the model was created by construction or obtained by estimation 
Method  Estimation command used 
InitialCondition  Handling of initial conditions during model estimation, returned as one of the following values:
This field is especially useful to view
how the initial conditions were handled when the 
Fit  Quantitative assessment of the estimation, returned as a structure. See Loss Function and Model Quality Metrics for more information on these quality metrics. The structure has these fields.

Parameters  Estimated values of model parameters 
OptionsUsed  Option set used for estimation. If no custom options were
configured, this is a set of default options. See 
RandState  State of the random number stream at the start of estimation. Empty,

DataUsed  Attributes of the data used for estimation, returned as a structure with the following fields.

Termination  Termination conditions for the iterative search used for prediction error minimization, returned as a structure with these fields.
For estimation methods that do not require numerical search
optimization, the 
For more information on using Report
, see Estimation Report.
ic
— Initial conditions
initialCondition
object  object array of initialCondition
values
Estimated initial conditions, returned as an initialCondition
object or an object array of
initialCondition
values.
For a singleexperiment data set,
ic
represents, in statespace form, the free response of the transfer function model (A and C matrices) to the estimated initial states (x_{0}).For a multipleexperiment data set with N_{e} experiments,
ic
is an object array of length N_{e} that contains one set ofinitialCondition
values for each experiment.
If oe
returns ic
values of
0
and the you know that you have nonzero initial conditions, set
the 'InitialCondition'
option in oeOptions
to 'estimate'
and pass the updated option set
to oe
. For
example:
opt = oeOptions('InitialCondition','estimate') [sys,ic] = oe(data,np,nz,opt)
'auto'
setting of 'InitialCondition'
uses
the 'zero'
method when the initial conditions have a negligible
effect on the overall estimationerror minimization process. Specifying
'estimate'
ensures that the software estimates values for
ic
.
For more information, see initialCondition
. For an example of using this argument, see Obtain Initial Conditions.
More About
OutputError (OE) Model
The general outputerror model structure is:
$$y(t)=\frac{B(q)}{F(q)}u(t{n}_{k})+e(t)$$
The orders of the outputerror model are:
$$\begin{array}{l}nb\text{:}B(q)={b}_{1}+{b}_{2}{q}^{1}+\mathrm{...}+{b}_{nb}{q}^{nb+1}\\ nf\text{:}F(q)=1+{f}_{1}{q}^{1}+\mathrm{...}+{f}_{nf}{q}^{nf}\end{array}$$
ContinuousTime OutputError Model
If data
is continuoustime frequencydomain data,
oe
estimates a continuoustime model with the following transfer
function:
$$G(s)=\frac{B(s)}{F(s)}=\frac{{b}_{nb}{s}^{(nb1)}+{b}_{nb1}{s}^{(nb2)}+\mathrm{...}+{b}_{1}}{{s}^{nf}+{f}_{nf}{s}^{(nf1)}+\mathrm{...}+{f}_{1}}$$
The orders of the numerator and denominator are nb
and
nf
, similar to the discretetime case. However, the sample delay
nk
does not exist in the continuous case, and you should not specify
nk
when you command the estimation. Instead, express any system delay
using the namevalue pair argument 'IODelay'
along with the system delay
in the time units that are stored in the property TimeUnit
. For example,
suppose that your continuous system has a delay of iod
seconds. Use
model = oe(data,[nb nf],'IODelay',iod)
.
Version History
Introduced before R2006aR2022b: Timedomain estimation data is accepted in the form of timetables and matrices
Most estimation, validation, analysis, and utility functions now accept timedomain
input/output data in the form of a single timetable that contains both input and output data
or a pair of matrices that contain the input and output data separately. These functions
continue to accept iddata
objects as a data source as well, for
both timedomain and frequencydomain data.
R2018a: Advanced Options are deprecated for SearchOptions
when SearchMethod
is 'lsqnonlin'
Specification of lsqnonlin
 related advanced options are deprecated,
including the option to invoke parallel processing when estimating using the
lsqnonlin
search method, or solver, in Optimization Toolbox™.
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