oe

Estimate output-error polynomial model using time-domain or frequency-domain data

Syntax

sys = oe(data,[nb nf nk])

sys = oe(data,[nb nf nk],Name,Value)

sys = oe(data,init_sys)

sys = oe(data,___,opt)

Description

Output-error (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 frequency-domain 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 continuous-time estimation, tfest provides faster and more accurate results, and is recommended.

sys = oe(data,[nb nf nk]) estimates an OE model sys, represented by

$$y(t)=\frac{B(q)}{F(q)}u(t-nk)+e(t)$$

y(t) is the output, u(t) is the input, and e(t) is the error.

oe estimates sys using the measured input-output data data, which can be in the time or the frequency domain. The orders [nb nf nk] define the number of parameters in each component of the estimated polynomial.

sys = oe(data,[nb nf nk],Name,Value) specifies model structure attributes using additional options specified by one or more name-value pair arguments.

sys = oe(data,init_sys) uses the linear system init_sys to configure the initial parameterization of sys.

sys = oe(data,___,opt) estimates a polynomial model using the option set opt to specify estimation behavior. You can use this syntax with any of the previous input-argument combinations.

Examples

Estimate OE Polynomial Model

Estimate an OE polynomial from time-domain data using two methods to specify input delay.

Load the estimation data.

load iddata1 z1

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(z1,[nb nf nk]);

Compare the simulated model response with the measured output.

compare(z1,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 name-value pair argument 'InputDelay' to specify the one-sample delay.

nk = 0;
sys1 = oe(z1,[nb nf nk],'InputDelay',1);
figure
compare(z1,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: 1.4809e-05
         LastImprovement: 5.1744e-06

The report includes information on the number of iterations and the reason the estimation stopped iterating.

Estimate Continuous-Time OE Model Using Frequency Response

Load the estimation data.

load oe_data1 data;

The idfrd object data contains the continuous-time frequency response for the following model:

$$G(s)=\frac{s+3}{s^3+2s^2+s+1}$$

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 high-order OE model from data collected by simulating a high-order 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 Band-Limited Discrete-Time Frequency-Domain Data

Load the estimation data data and sample time Ts.

load oe_data2.mat data Ts

An iddata object data contains the discrete-time frequency response for the following model:

$$G(s)=\frac{1000}{s+500}$$

View the estimation sample time Ts that you loaded.

Ts

Ts = 1.0000e-03

This value matches the property data.Ts.

data.Ts

ans = 1.0000e-03

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 continuous-time data.

data.Ts = 0;

Estimate the continuous model.

nb = 1;
nf = 3;
sys = oe(data,[nb nf],opt);

Input Arguments

data — Estimation data

iddata object | frd object | idfrd object

Estimation data, specified as an iddata object, an frd object, or an idfrd object.

For time-domain estimation, data must be an iddata object containing the input and output signal values.

For frequency-domain estimation, data can be one of the following:

• Recorded frequency response data (frd or idfrd)

• iddata object with properties specified as follows:

  • InputData — Fourier transform of the input signal

  • OutputData — Fourier transform of the output signal

  • Domain — 'Frequency'

Time-domain 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 discrete-time models from time-domain data or discrete-time frequency-domain data. Use tfest to compute continuous-time models.

[nb nf nk] — OE model orders

integer row vector | row vector of integer matrices

OE model orders, specified as a 1-by-3 vector or a vector of integer matrices.

For a system represented by

$$y(t)=\frac{B(q)}{F(q)}u(t-nk)+e(t)$$

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-by-N_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-by-N_u matrix.

• nk — Input delay, expressed as the number of samples. nk is an N_y-by-N_u matrix. The delay appears as leading zeros of the B polynomial.

For estimation using continuous-time frequency-domain data, specify only [nb nf] and omit nk. For an example, see Estimate Continuous-Time 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, set init_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 Band-Limited Discrete-Time Frequency-Domain Data.

Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is 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 Name1,Value1,...,NameN,ValueN.

Example: 'InputDelay',1

'InputDelay' — Input delays

0 (default) | positive integer vector | integer scalar

Input delays for each input channel, specified as the comma-separated pair consisting of 'InputDelay' and a numeric vector.

• For continuous-time models, specify 'InputDelay' in the time units stored in the TimeUnit property.

• For discrete-time models, specify 'InputDelay' in integer multiples of the sample time Ts. For example, setting 'InputDelay' to 3 specifies a delay of three sampling periods.

For a system with N_u inputs, set InputDelay to an N_u-by-1 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 input-output pair, specified as the comma-separated pair consisting of 'IODelay' and a numeric array.

• For continuous-time models, specify 'IODelay' in the time units stored in the TimeUnit property.

• For discrete-time models, specify 'IODelay' in integer multiples of the sample time Ts. For example, setting 'IODelay' to 4 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-by-N_u matrix. Each entry is an integer value representing the transport delay for the corresponding input-output 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(nk-1,0) leading zeros as input-output 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 discrete-time model when estimated from time-domain data. For continuous-time model identification using time-domain 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: 'zero' — The initial conditions were set to zero. 'estimate' — The initial conditions were treated as independent estimation parameters. 'backcast' — The initial conditions were estimated using the best least squares fit. This field is especially useful to view how the initial conditions were handled when the InitialCondition option in the estimation option set is 'auto'.
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 the following fields: Field Description FitPercent Normalized root mean squared error (NRMSE) measure of how well the response of the model fits the estimation data, expressed as the percentage fit = 100(1-NRMSE). LossFcn Value of the loss function when the estimation completes. MSE Mean squared error (MSE) measure of how well the response of the model fits the estimation data. FPE Final prediction error for the model. AIC Raw Akaike Information Criteria (AIC) measure of model quality. AICc Small sample-size corrected AIC. nAIC Normalized AIC. BIC Bayesian Information Criteria (BIC).
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 oeOptions for more information.
RandState	State of the random number stream at the start of estimation. Empty, [], if randomization was not used during estimation. For more information, see rng in the MATLAB® documentation.
DataUsed	Attributes of the data used for estimation, returned as a structure with the following fields: Field Description Name Name of the data set. Type Data type. Length Number of data samples. Ts Sample time. InterSample Input intersample behavior, returned as one of the following values: '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. InputOffset Offset removed from time-domain input data during estimation. For nonlinear models, it is []. OutputOffset Offset removed from time-domain output data during estimation. For nonlinear models, it is [].
Termination	Termination conditions for the iterative search used for prediction error minimization. Structure with the following fields: Field Description WhyStop Reason for terminating the numerical search. Iterations Number of search iterations performed by the estimation algorithm. FirstOrderOptimality $$\infty$$-norm of the gradient search vector when the search algorithm terminates. FcnCount Number of times the objective function was called. UpdateNorm Norm of the gradient search vector in the last iteration. Omitted when the search method is 'lsqnonlin' or 'fmincon'. LastImprovement Criterion improvement in the last iteration, expressed as a percentage. Omitted when the search method is 'lsqnonlin' or 'fmincon'. Algorithm Algorithm used by 'lsqnonlin' or 'fmincon' search method. Omitted when other search methods are used. For estimation methods that do not require numerical search optimization, the Termination field is omitted.

For more information on using Report, see Estimation Report.

More About

Output-Error (OE) Model

The general output-error model structure is:

$$y(t)=\frac{B(q)}{F(q)}u(t-n_k)+e(t)$$

The orders of the output-error model are:

$$\begin{array}{l}nb\text{: }B(q)=b_1+b_2{q}^{-1}+\mathrm{...}+b_{nb}{q}^{-nb+1}\\ nf\text{: }F(q)=1+f_1{q}^{-1}+\mathrm{...}+f_{nf}{q}^{-nf}\end{array}$$

Continuous-Time Output-Error Model

If data is continuous-time frequency-domain data, oe estimates a continuous-time model with the following transfer function:

$$G(s)=\frac{B(s)}{F(s)}=\frac{b_{nb}{s}^{(nb-1)}+b_{nb-1}{s}^{(nb-2)}+\mathrm{...}+b_1}{s^{nf}+f_{nf}{s}^{(nf-1)}+\mathrm{...}+f_1}$$

The orders of the numerator and denominator are nb and nf, similar to the discrete-time 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 name-value 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).

Extended Capabilities

Automatic Parallel Support

Accelerate code by automatically running computation in parallel using Parallel Computing Toolbox™.

Parallel computing support is available for estimation using the lsqnonlin search method (requires Optimization Toolbox™). To enable parallel computing, use oeOptions, set SearchMethod to 'lsqnonlin', and set SearchOptions.Advanced.UseParallel to true.

For example:

opt = oeOptions;
opt.SearchMethod = 'lsqnonlin';
opt.SearchOptions.Advanced.UseParallel = true;