Description Usage Arguments Details Value References Examples
Fit an ARMAX model of the specified order given the input-output data
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x |
an object of class |
order |
Specification of the orders: the four integer components (na,nb,nc,nk) are the order of polynolnomial A, order of polynomial B + 1, order of the polynomial C,and the input-output delay respectively |
init_sys |
Linear polynomial model that configures the initial parameterization.
Must be an ARMAX model. Overrules the |
intNoise |
Logical variable indicating whether to add integrators in
the noise channel (Default= |
options |
Estimation Options, setup using |
SISO ARMAX models are of the form
y[k] + a_1 y[k-1] + … + a_{na} y[k-na] = b_{nk} u[k-nk] + … + b_{nk+nb} u[k-nk-nb] + c_{1} e[k-1] + … c_{nc} e[k-nc] + e[k]
The function estimates the coefficients using non-linear least squares
(Levenberg-Marquardt Algorithm)
The data is expected to have no offsets or trends. They can be removed
using the detrend
function.
An object of class estpoly
containing the following elements:
sys |
an |
fitted.values |
the predicted response |
residuals |
the residuals |
input |
the input data used |
call |
the matched call |
stats |
A list containing the following fields: |
options |
Option set used for estimation. If no custom options were configured, this is a set of default options |
termination |
Termination conditions for the iterative
search used for prediction error minimization:
|
Arun K. Tangirala (2015), Principles of System Identification: Theory and Practice, CRC Press, Boca Raton. Sections 14.4.1, 21.6.2
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