Description Usage Arguments Details Value References Examples
Fit an output-error model of the specified order given the input-output data
1 |
x |
an object of class |
order |
Specification of the orders: the four integer components (nb,nf,nk) are order of polynomial B + 1, order of the polynomial F, and the input-output delay respectively |
init_sys |
Linear polynomial model that configures the initial parameterization.
Must be an OE model. Overrules the |
options |
Estimation Options, setup using
|
SISO OE models are of the form
y[k] + f_1 y[k-1] + … + f_{nf} y[k-nf] = b_{nk} u[k-nk] + … + b_{nk+nb} u[k-nk-nb] + f_{1} e[k-1] + … f_{nf} e[k-nf] + 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, 17.5.2, 21.6.3
1 2 3 4 5 |
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.