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 