Access polynomial coefficients and uncertainties of identified model
[A,B,C,D,F] = polydata(sys)
[A,B,C,D,F,dA,dB,dC,dD,dF]
= polydata(sys)
___ = polydata(sys,J1,...,JN)
___ = polydata(___,'cell')
[
returns
the coefficients of the polynomials A,B,C,D,F
] = polydata(sys
)A
, B
, C
, D
,
and F
that describe the identified model sys
.
The polynomials describe the idpoly
representation
of sys
as follows.
For discretetime sys
:
$$A\left({q}^{1}\right)y\left(t\right)=\frac{B\left({q}^{1}\right)}{F\left({q}^{1}\right)}u\left(tnk\right)+\frac{C\left({q}^{1}\right)}{D\left({q}^{1}\right)}e\left(t\right).$$
u(t) are the inputs to sys
. y(t)
are the outputs. e(t) is a white
noise disturbance.
For continuoustime sys
:
$$A\left(s\right)Y\left(s\right)=\frac{B\left(s\right)}{F\left(s\right)}U\left(s\right){e}^{\tau s}+\frac{C\left(s\right)}{D\left(s\right)}E\left(s\right).$$
U(s) are the Laplace transformed
inputs to sys
. Y(s)
are the Laplace transformed outputs. E(s)
is the Laplace transform of a white noise disturbance.
If sys
is an identified model that is not
an idpoly
model, polydata
converts sys
to idpoly
form
to extract the polynomial coefficients.
[
also returns the uncertainties A,B,C,D,F
,dA,dB,dC,dD,dF
]
= polydata(sys
)dA
, dB
, dC
, dD
,
and dF
of each of the corresponding polynomial
coefficients of sys
.
___ = polydata(
returns
the polynomial coefficients for the sys
,J1,...,JN
)J1,...,JN
entry
in the array sys
of identified models.
___ = polydata(___,'cell')
returns
all polynomials as cell arrays of double vectors, regardless of the
input and output dimensions of sys
.

Identified model or array of identified models. 

Indices selecting a particular model from an Ndimensional array 

Polynomial coefficients of the
Each entry in a cell array is a row vector that contains the coefficients of the corresponding polynomial. The polynomial coefficients are ordered the same way as the SISO case. 

Uncertainties in the estimated polynomial coefficients of
Each entry in 