rarx

(To be removed) Estimate parameters of ARX or AR models recursively

Note

rarx will be removed in a future release. Use recursiveAR or recursiveARX instead.

Syntax

thm = rarx(z,nn,adm,adg) [thm,yhat,P,phi] = rarx(z,nn,adm,adg,th0,P0,phi0)

Description

thm = rarx(z,nn,adm,adg) estimates the parameters thm of single-output ARX model from input-output data z and model orders nn using the algorithm specified by adm and adg. If z is a time series y and nn = na, rarx estimates the parameters of a single-output AR model.

[thm,yhat,P,phi] = rarx(z,nn,adm,adg,th0,P0,phi0) estimates the parameters thm, the predicted output yhat, final values of the scaled covariance matrix of the parameters P, and final values of the data vector phi of single-output ARX model from input-output data z and model orders nn using the algorithm specified by adm and adg. If z is a time series y and nn = na, rarx estimates the parameters of a single-output AR model.

Input Arguments

z

Name of the matrix iddata object that represents the input-output data or a matrix z = [y u], where y and u are column vectors.

For multiple-input models, the u matrix contains each input as a column vector:

u = [u1 ... unu]

nn

For input-output models, specifies the structure of the ARX model as:

nn = [na nb nk]

where na and nb are the orders of the ARX model, and nk is the delay.

For multiple-input models, nb and nk are row vectors that define orders and delays for each input.

For time-series models, nn = na, where na is the order of the AR model.

Note

The delay nk must be larger than 0. If you want nk = 0, shift the input sequence appropriately and use nk = 1 (see nkshift).

adm and adg

adm = 'ff' and adg = lam specify the forgetting factor algorithm with the forgetting factor λ=lam. This algorithm is also known as recursive least squares (RLS). In this case, the matrix P has the following interpretation: R₂/2 * P is approximately equal to the covariance matrix of the estimated parameters.R₂ is the variance of the innovations (the true prediction errors e(t)).

adm ='ug' and adg = gam specify the unnormalized gradient algorithm with gain gamma = gam. This algorithm is also known as the normalized least mean squares (LMS).

adm ='ng' and adg = gam specify the normalized gradient or normalized least mean squares (NLMS) algorithm. In these cases, P is not applicable.

adm ='kf' and adg =R1 specify the Kalman filter based algorithm with R₂=1 and R₁ = R1. If the variance of the innovations e(t) is not unity but R₂; then R₂* P is the covariance matrix of the parameter estimates, while R₁ = R1 /R₂ is the covariance matrix of the parameter changes.

th0

Initial value of the parameters in a row vector, consistent with the rows of thm.

Default: All zeros.

P0

Initial values of the scaled covariance matrix of the parameters.

Default: 10⁴ times the identity matrix.

phi0

The argument phi0 contains the initial values of the data vector:

φ(t) = [y(t–1),...,y(t–na),u(t–1),...,u(t–nb–nk+1)]

If z = [y(1),u(1); ... ;y(N),u(N)], phi0 = φ(1) and phi = φ(N). For online use of rarx, use phi0, th0, and P0 as the previous outputs phi, thm (last row), and P.

Default: All zeros.

Output Arguments

thm

Estimated parameters of the model. The kth row of thm contains the parameters associated with time k; that is, the estimate parameters are based on the data in rows up to and including row k in z. Each row of thm contains the estimated parameters in the following order:

thm(k,:) = [a1,a2,...,ana,b1,...,bnb]

For a multiple-input model, the b are grouped by input. For example, the b parameters associated with the first input are listed first, and the b parameters associated with the second input are listed next.

yhat

Predicted value of the output, according to the current model; that is, row k of yhat contains the predicted value of y(k) based on all past data.

P

Final values of the scaled covariance matrix of the parameters.

phi

phi contains the final values of the data vector:

φ(t) = [y(t–1),...,y(t–na),u(t–1),...,u(t–nb–nk+1)]

Examples

Adaptive noise canceling: The signal y contains a component that originates from a known signal r. Remove this component by recursively estimating the system that relates r to y using a sixth-order FIR model and the NLMS algorithm.

z = [y r];
[thm,noise] = rarx(z,[0 6 1],'ng',0.1);
% noise is the adaptive estimate of the noise
% component of y
plot(y-noise)

If this is an online application, you can plot the best estimate of the signal y - noise at the same time as the data y and u become available, use the following code:

phi = zeros(6,1);
P=1000*eye(6);
th = zeros(1,6);
axis([0 100 -2 2]);
plot(0,0,'*'), hold on
% Use a while loop
while ~abort
[y,r,abort] = readAD(time);
[th,ns,P,phi] = rarx([y r],'ff',0.98,th,P,phi);
plot(time,y-ns,'*')
time = time + Dt
end

This example uses a forgetting factor algorithm with a forgetting factor of 0.98. readAD is a function that reads the value of an A/D converter at the indicated time instant.

More About

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ARX Model Structure

The general ARX model structure is:

$A (q) y (t) = B (q) u (t - n k) + e (t)$

The orders of the ARX model are:

$\begin{matrix} n a : A (q) = 1 + a_{1} q^{- 1} + ... + a_{n a} q^{- n a} \\ n b : B (q) = b_{1} + b_{2} q^{- 1} + ... + b_{n b} q^{- n b + 1} \end{matrix}$

Models with several inputs are defined, as follows:

A(q)y(t) = B₁(q)u₁(t–nk₁)+...+B_nuu_nu(t–nk_nu)+e(t)

Version History

Introduced before R2006a