Documentation

# recursiveOE

Create System object for online parameter estimation of Output-Error polynomial model

## Syntax

obj = recursiveOE
obj = recursiveOE(Orders)
obj = recursiveOE(Orders,B0,F0)
obj = recursiveOE(___,Name,Value)

## Description

Use recursiveOE command for parameter estimation with real-time data. If all data necessary for estimation is available at once, and you are estimating a time-invariant model, use the offline estimation command, oe.

obj = recursiveOE creates a System object™ for online parameter estimation of a default single-input-single output (SISO) Output-Error model structure. The default model structure has polynomials of order 1 and initial polynomial coefficient values eps.

After creating the object, use the step command to update model parameter estimates using recursive estimation algorithms and real-time data.

obj = recursiveOE(Orders) specifies the polynomial orders of the Output-Error model to be estimated.

obj = recursiveOE(Orders,B0,F0) specifies the polynomial orders and initial values of the polynomial coefficients. Specify initial values to potentially avoid local minima during estimation. If the initial values are small compared to the default InitialParameterCovariance property value, and you have confidence in your initial values, also specify a smaller InitialParameterCovariance.

obj = recursiveOE(___,Name,Value) specifies additional attributes of the Output-Error model structure and recursive estimation algorithm using one or more Name,Value pair arguments.

## Object Description

recursiveOE creates a System object for online parameter estimation of SISO Output-Error polynomial models using a recursive estimation algorithm.

A System object is a specialized MATLAB® object designed specifically for implementing and simulating dynamic systems with inputs that change over time. System objects use internal states to store past behavior, which is used in the next computational step.

After you create a System object, you use commands to process data or obtain information from or about the object. System objects use a minimum of two commands to process data — a constructor to create the object and the step command to update object parameters using real-time data. This separation of declaration from execution lets you create multiple, persistent, reusable objects, each with different settings.

You can use the following commands with the online estimation System objects in System Identification Toolbox™:

CommandDescription
step

Update model parameter estimates using recursive estimation algorithms and real-time data.

step puts the object into a locked state. In a locked state, you cannot change any nontunable properties or input specifications, such as model order, data type, or estimation algorithm. During execution, you can only change tunable properties.

release

Unlock the System object. Use this command to enable setting of nontunable parameters.

reset

Reset the internal states of a locked System object to the initial values, and leave the object locked.

clone

Create another System object with the same object property values.

Do not create additional objects using syntax obj2 = obj. Any changes made to the properties of the new object created this way (obj2) also change the properties of the original object (obj).

isLocked

Query locked status for input attributes and nontunable properties of the System object.

Use the recursiveOE command to create an online estimation System object. Then estimate the Output-Error polynomial model parameters (B and F) and output using the step command with incoming input and output data, u and y.

[B,F,EstimatedOutput] = step(obj,y,u)

For recursiveOE object properties, see Properties.

## Examples

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Create a System object for online parameter estimation of a Output-Error polynomial model using recursive estimation algorithms.

obj = recursiveOE;

The Output-Error model has a default structure with polynomials of order 1 and initial polynomial coefficient values, eps.

Load the estimation data. In this example, use a static data set for illustration.

load iddata1 z1;
output = z1.y;
input = z1.u;

Estimate Output-Error model parameters online using step.

for i = 1:numel(input)
[B,F,EstimatedOutput] = step(obj,output(i),input(i));
end

View the current estimated values of polynomial F coefficients.

obj.F
ans = 1×2

1.0000   -0.7618

View the current covariance estimate of the parameters.

obj.ParameterCovariance
ans = 2×2

0.0024    0.0002
0.0002    0.0001

View the current estimated output.

EstimatedOutput
EstimatedOutput = -4.1866

Specify Output-Error polynomial model orders and delays.

nb = 1;
nf = 2;
nk = 1;

Create a System object for online estimation of Output-Error polynomial model with the specified orders and delays.

obj = recursiveOE([nb nf nk]);

Specify Output-Error polynomial model orders and delays.

nb = 1;
nf = 2;
nk = 1;

Create a System object for online estimation of Output-Error model with known initial polynomial coefficients.

B0 = [0 1];
F0 = [1 0.7 0.8];
obj = recursiveOE([nb nf nk],B0,F0);

Specify the initial parameter covariance.

obj.InitialParameterCovariance = 0.1;

InitialParameterCovariance represents the uncertainty in your guess for the initial parameters. Typically, the default InitialParameterCovariance (10000) is too large relative to the parameter values. This results in initial guesses being given less importance during estimation. If you have confidence in the initial parameter guesses, specify a smaller initial parameter covariance.

Create a System object that uses the unnormalized gradient algorithm for online parameter estimation of an Output-Error model.

obj = recursiveOE([1 2 1],'EstimationMethod','Gradient');

## Input Arguments

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Model orders and delays of a Output-Error polynomial model , specified as a 1-by-3 vector of integers, [nb nf nk].

• nb — Order of the polynomial B(q) + 1, specified as a positive integer.

• nf — Order of the polynomial F(q), specified as a nonnegative integer.

• nk — Input-output delay, specified as a positive integer. nk is number of input samples that occur before the input affects the output. nk is expressed as fixed leading zeros of the B polynomial.

Initial value of polynomial coefficients, specified as row vectors of real values with elements in order of ascending powers of q-1.

• B0 — Initial guess for the coefficients of the polynomial B(q), specified as a 1-by-(nb+nk) vector with nk leading zeros.

• F0 — Initial guess for the coefficients of the polynomial F(q), specified as a 1-by-(nf+1) vector with 1 as the first element.

The coefficients in F0 must define a stable discrete-time polynomial with roots within a unit disk. For example,

F0 = [1 0.5 0.5];
all(abs(roots(F0))<1)
ans =

1

Specifying as [], uses the default value of eps for the polynomial coefficients.

### Note

If the initial parameter values are much smaller than InitialParameterCovariance, these initial values are given less importance during estimation. Specify a smaller initial parameter covariance if you have high confidence in the initial parameter values. This statement applies only for infinite-history estimation. Finite-history estimation does not use InitialParameterCovariance.

### 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.

Use Name,Value arguments to specify writable properties of recursiveOE System object during object creation. For example, obj = recursiveOE([1 2 1],'EstimationMethod','Gradient') creates a System object to estimate a Output-Error polynomial model using the 'Gradient' recursive estimation algorithm.

## Properties

recursiveOE System object properties consist of read-only and writable properties. The writable properties are tunable and nontunable properties. The nontunable properties cannot be changed when the object is locked, that is, after you use the step command.

Use Name,Value arguments to specify writable properties of recursiveOE objects during object creation. After object creation, use dot notation to modify the tunable properties.

obj = recursiveOE;
obj.ForgettingFactor = 0.99;

B

Estimated coefficients of polynomial B(q), returned as a vector of real values specified in order of ascending powers of q-1.

B is a read-only property and is initially empty after you create the object. It is populated after you use the step command for online parameter estimation.

F

Estimated coefficients of polynomial F(q), returned as a vector of real values specified in order of ascending powers of q-1.

F is a read-only property and is initially empty after you create the object. It is populated after you use the step command for online parameter estimation.

InitialB

Initial values for the coefficients of polynomial B(q) of order nb-1, specified as a row vector of length nb+nk, with nk leading zeros. nk is the input-output delay. Specify the coefficients in order of ascending powers of q-1.

If the initial guesses are much smaller than the default InitialParameterCovariance, 10000, the initial guesses are given less importance during estimation. In that case, specify a smaller initial parameter covariance.

InitialB is a tunable property. You can change it when the object is in a locked state.

Default: [0 eps]

InitialF

Initial values for the coefficients of polynomial F(q) of order nf, specified as a row vector of length nf+1, with 1 as the first element. Specify the coefficients in order of ascending powers of q-1.

The coefficients in InitialF must define a stable discrete-time polynomial with roots within a unit circle. For example,

InitialF = [1 0.9 0.8];
all(abs(roots(InitialF))<1)
ans =

1

If the initial guesses are much smaller than the default InitialParameterCovariance, 10000, the initial guesses are given less importance during estimation. In that case, specify a smaller initial parameter covariance.

InitialF is a tunable property. You can change it when the object is in a locked state.

Default: [1 eps]

InitialOutputs

Initial values of the measured outputs buffer in finite-history estimation, specified as 0 or as a (W+nf)-by-1 vector, where W is the window length and nf is the order of the polynomial F(q) that you specify when constructing the object.

The InitialOutputs property provides a means of controlling the initial behavior of the algorithm.

When InitialOutputs is set to 0, the object populates the buffer with zeros.

If the initial buffer is set to 0 or does not contain enough information, you see a warning message during the initial phase of your estimation. The warning should clear after a few cycles. The number of cycles it takes for sufficient information to be buffered depends upon the order of your polynomials and your input delays. If the warning persists, you should evaluate the content of your signals.

Specify InitialOutputs only when History is Finite.

InitialOutputs is a tunable property. You can change InitialOutputs when the object is in a locked state.

Default: 0

InitialInputs

Initial values of the inputs in the finite history window, specified as 0 or as a (W+nb+nk-1)-by-1 vector, where W is the window length. nb is the vector of B(q) polynomial orders and nk is vector of input delays that you specify when constructing the recursiveOE object.

The InitialInputs property provides a means of controlling the initial behavior of the algorithm.

When the InitialInputs is set to 0, the object populates the buffer with zeros.

If the initial buffer is set to 0 or does not contain enough information, you see a warning message during the initial phase of your estimation. The warning should clear after a few cycles. The number of cycles it takes for sufficient information to be buffered depends upon the order of your polynomials and your input delays. If the warning persists, you should evaluate the content of your signals.

Specify InitialInputs only when History is Finite.

InitialInputs is a tunable property. You can change InitialInputs when the object is in a locked state.

Default: 0

ParameterCovariance

Estimated covariance P of the parameters, returned as an N-by-N symmetric positive-definite matrix. N is the number of parameters to be estimated. The software computes P assuming that the residuals (difference between estimated and measured outputs) are white noise, and the variance of these residuals is 1.

ParameterCovariance is applicable only when EstimationMethod is 'ForgettingFactor' or 'KalmanFilter' or when History is Finite.

The interpretation of P depends on your settings for the History and EstimationMethod properties.

• If History is Infinite, then your EstimationMethod selection results in one of the following:

• 'ForgettingFactor' — (R2/2)P is approximately equal to the covariance matrix of the estimated parameters, where R2 is the true variance of the residuals.

• 'KalmanFilter'R2P is the covariance matrix of the estimated parameters, and R1 /R2 is the covariance matrix of the parameter changes. Here, R1 is the covariance matrix that you specify in ProcessNoiseCovariance.

• If History is Finite (sliding-window estimation) — R2P is the covariance of the estimated parameters. The sliding-window algorithm does not use this covariance in the parameter-estimation process. However, the algorithm does compute the covariance for output so that you can use it for statistical evaluation.

ParameterCovariance is a read-only property and is initially empty after you create the object. It is populated after you use the step command for online parameter estimation.

InitialParameterCovariance

Covariance of the initial parameter estimates, specified as one of the following:

• Real positive scalar, α — Covariance matrix is an N-by-N diagonal matrix, with α as the diagonal elements. N is the number of parameters to be estimated.

• Vector of real positive scalars, [α1,...,αN] — Covariance matrix is an N-by-N diagonal matrix, with [α1,...,αN] as the diagonal elements.

• N-by-N symmetric positive-definite matrix.

InitialParameterCovariance represents the uncertainty in the initial parameter estimates. For large values of InitialParameterCovariance, less importance is placed on the initial parameter values and more on the measured data during beginning of estimation using step.

Use only when EstimationMethod is 'ForgettingFactor' or 'KalmanFilter'.

InitialParameterCovariance is a tunable property. You can change it when the object is in a locked state.

Default: 10000

EstimationMethod

Recursive estimation algorithm used for online estimation of model parameters, specified as one of the following values:

• 'ForgettingFactor' — Algorithm used for parameter estimation

• 'KalmanFilter' — Algorithm used for parameter estimation

• 'NormalizedGradient' — Algorithm used for parameter estimation

• 'Gradient' — Unnormalized gradient algorithm used for parameter estimation

Forgetting factor and Kalman filter algorithms are more computationally intensive than gradient and unnormalized gradient methods. However, they have better convergence properties. For information about these algorithms, see Recursive Algorithms for Online Parameter Estimation.

These methods all use an infinite data history, and are available only when History is 'Infinite'.

EstimationMethod is a nontunable property. You cannot change it during execution, that is, after the object is locked using the step command.

Default: Forgetting Factor

ForgettingFactor

Forgetting factor, λ, relevant for parameter estimation, specified as a scalar in the range (0,1].

Suppose that the system remains approximately constant over T0 samples. You can choose λ such that:

${T}_{0}=\frac{1}{1-\lambda }$

• Setting λ = 1 corresponds to “no forgetting” and estimating constant coefficients.

• Setting λ < 1 implies that past measurements are less significant for parameter estimation and can be “forgotten”. Set λ < 1 to estimate time-varying coefficients.

Typical choices of λ are in the range [0.98 0.995].

Use only when EstimationMethod is 'ForgettingFactor'.

ForgettingFactor is a tunable property. You can change it when the object is in a locked state.

Default: 1

EnableAdapation

Enable or disable parameter estimation, specified as one of the following:

• true or 1— The step command estimates the parameter values for that time step and updates the parameter values.

• false or 0 — The step command does not update the parameters for that time step and instead outputs the last estimated value. You can use this option when your system enters a mode where the parameter values do not vary with time.

### Note

If you set EnableAdapation to false, you must still execute the step command. Do not skip step to keep parameter values constant, because parameter estimation depends on current and past I/O measurements. step ensures past I/O data is stored, even when it does not update the parameters.

EnableAdapation is a tunable property. You can change it when the object is in a locked state.

Default: true

DataType

Floating point precision of parameters, specified as one of the following values:

• 'double' — Double-precision floating point

• 'single' — Single-precision floating point

Setting DataType to 'single' saves memory, but leads to loss of precision. Specify DataType based on the precision required by the target processor where you will deploy generated code.

DataType is a nontunable property. It can only be set during object construction using Name,Value arguments and cannot be changed afterward.

Default: 'double'

ProcessNoiseCovariance

Covariance matrix of parameter variations, specified as one of the following:

• Real nonnegative scalar, α — Covariance matrix is an N-by-N diagonal matrix, with α as the diagonal elements.

• Vector of real nonnegative scalars, [α1,...,αN] — Covariance matrix is an N-by-N diagonal matrix, with [α1,...,αN] as the diagonal elements.

• N-by-N symmetric positive semidefinite matrix.

N is the number of parameters to be estimated.

ProcessNoiseCovariance is applicable when EstimationMethod is 'KalmanFilter'.

Kalman filter algorithm treats the parameters as states of a dynamic system and estimates these parameters using a Kalman filter. ProcessNoiseCovariance is the covariance of the process noise acting on these parameters. Zero values in the noise covariance matrix correspond to estimating constant coefficients. Values larger than 0 correspond to time-varying parameters. Use large values for rapidly changing parameters. However, the larger values result in noisier parameter estimates.

ProcessNoiseCovariance is a tunable property. You can change it when the object is in a locked state.

Default: 0.1

AdaptationGain

Adaptation gain, γ, used in gradient recursive estimation algorithms, specified as a positive scalar.

AdaptationGain is applicable when EstimationMethod is 'Gradient' or 'NormalizedGradient'.

Specify a large value for AdaptationGain when your measurements have a high signal-to-noise ratio.

AdaptationGain is a tunable property. You can change it when the object is in a locked state.

Default: 1

NormalizationBias

Bias in adaptation gain scaling used in the 'NormalizedGradient' method, specified as a nonnegative scalar.

NormalizationBias is applicable when EstimationMethod is 'NormalizedGradient'.

The normalized gradient algorithm divides the adaptation gain at each step by the square of the two-norm of the gradient vector. If the gradient is close to zero, this can cause jumps in the estimated parameters. NormalizationBias is the term introduced in the denominator to prevent these jumps. Increase NormalizationBias if you observe jumps in estimated parameters.

NormalizationBias is a tunable property. You can change it when the object is in a locked state.

Default: eps

History

Data history type defining which type of recursive algorithm you use, specified as:

• 'Infinite' — Use an algorithm that aims to minimize the error between the observed and predicted outputs for all time steps from the beginning of the simulation.

• 'Finite' — Use an algorithm that aims to minimize the error between the observed and predicted outputs for a finite number of past time steps.

Algorithms with infinite history aim to produce parameter estimates that explain all data since the start of the simulation. These algorithms still use a fixed amount of memory that does not grow over time. The object provides multiple algorithms of the 'Infinite' History type. Specifying this option activates the EstimationMethod property with which you specify an algorithm.

Algorithms with finite history aim to produce parameter estimates that explain only a finite number of past data samples. This method is also called sliding-window estimation. The object provides one algorithm of the 'Finite' type. Specifying this option activates the WindowLength property that sizes the window.

For more information on recursive estimation methods, see Recursive Algorithms for Online Parameter Estimation.

History is a nontunable property. It can be set only during object construction using Name,Value arguments and cannot be changed afterward.

Default: 'Infinite'

WindowLength

Window size determining the number of time samples to use for the sliding-window estimation method, specified as a positive integer. Specify WindowLength only when History is Finite.

Choose a window size that balances estimation performance with computational and memory burden. Sizing factors include the number and time variance of the parameters in your model. Always specify Window Length in samples, even if you are using frame-based input processing.

WindowLength must be greater than or equal to the number of estimated parameters.

Suitable window length is independent of whether you are using sample-based or frame-based input processing (see InputProcessing). However, when using frame-based processing, your window length must be greater than or equal to the number of samples (time steps) contained in the frame.

WindowLength is a nontunable property. It can be set only during object construction using Name,Value arguments and cannot be changed afterward.

Default: 200

InputProcessing

Option for sample-based or frame-based input processing, specified as a character vector or string.

• Sample-based processing operates on signals streamed one sample at a time.

• Frame-based processing operates on signals containing samples from multiple time steps. Many machine sensor interfaces package multiple samples and transmit these samples together in frames. Frame-based processing allows you to input this data directly without having to first unpack it.

Your InputProcessing specification impacts the dimensions for the input and output signals when using the step command:

[theta,EstimatedOutput] = step(obj,y,u)

• Sample-based

• y,u, and EstimatedOutput are scalars.

• Frame-based with M samples per frame

• y,u, and EstimatedOutput are M-by-1 vectors.

InputProcessing is a nontunable property. It can be set only during object construction using Name,Value arguments and cannot be changed afterward.

Default: 'Sample-based'

## Output Arguments

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System object for online parameter estimation of SISO Output-Error polynomial model, returned as a recursiveOE System object. This object is created using the specified model orders and properties. Use step command to estimate the coefficients of the Output-Error model polynomials. You can then access the estimated coefficients and parameter covariance using dot notation. For example, type obj.B to view the estimated B polynomial coefficients.

## More About

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### Output-Error Model Structure

The general Output-Error model structure is:

$y\left(t\right)=\frac{B\left(q\right)}{F\left(q\right)}u\left(t-{n}_{k}\right)+e\left(t\right)$

The orders of the Output-Error model are:

## Tips

• Starting in R2016b, instead of using the step command to update model parameter estimates, you can call the System object with input arguments, as if it were a function. For example, [B,F,EstimatedOutput] = step(obj,y,u) and [B,F,EstimatedOutput] = obj(y,u) perform equivalent operations.

Get trial now