Linear approximation of nonlinear ARX and Hammerstein-Wiener models for given input
lm = linapp(nlmodel,u)
lm = linapp(nlmodel,umin,umax,nsample)
lm = linapp(nlmodel,u) computes a linear
approximation of a nonlinear ARX or Hammerstein-Wiener model by simulating
the model output for the input signal u, and estimating
a linear model lm from u and
the simulated output signal. lm is an idpoly model.
lm = linapp(nlmodel,umin,umax,nsample) computes
a linear approximation of a nonlinear ARX or Hammerstein-Wiener model
by first generating the input signal as a uniformly distributed white
noise from the magnitude range umin and umax and
(optionally) the number of samples.
nlmodelName of the idnlarx or idnlhw model
object you want to linearize.
uInput signal as an iddata object
or a real matrix.
Dimensions of u must match the number of
inputs in nlmodel.
[umin,umax]Minimum and maximum input values for generating white-noise
input with a magnitude in this rectangular range. The sample length
of this signal is nsample.
nsampleOptional argument when you specify [umin,umax].
Specifies the length of the white-noise input.
Default: 1024.
idnlarx | idnlarx/findop | idnlarx/linearize | idnlhw | idnlhw/findop | idnlhw/linearize