polyest
Estimate polynomial model using time- or frequency-domain data
Syntax
sys = polyest(data,[na nb nc nd nf nk])
sys =保利(数据,(na nb数控nd nf nk)名称、值)
sys = polyest(data,init_sys)
sys = polyest(___, opt)
[sys,ic] = polyest(___)
Description
estimates a polynomial model,sys
= polyest(data
,[na
nb
nc
nd
nf
nk
])sys
, using the time- or frequency-domain data,data
.
sys
is of the form
A(q),B(q),F(q),C(q) andD(q) are polynomial matrices.u(t) is the input, andnk
is the input delay.y(t) is the output ande(t) is the disturbance signal.na
,nb
,nc
,nd
andnf
are the orders of theA(q),B(q),C(q),D(q) andF(q) polynomials, respectively.
estimates a polynomial model with additional attributes of the estimated model structure specified by one or moresys
= polyest(data
,[na
nb
nc
nd
nf
nk
],Name,Value
)Name,Value
pair arguments.
estimates a polynomial model using the linear systemsys
= polyest(data
,init_sys
)init_sys
to configure the initial parameterization.
estimates a polynomial model using the option set,sys
= polyest(___,opt
)opt
, to specify estimation behavior.
[
returns the estimated initial conditions as ansys
,ic
] = polyest(___)initialCondition
object. Use this syntax if you plan to simulate or predict the model response using the same estimation input data and then compare the response with the same estimation output data. Incorporating the initial conditions yields a better match during the first part of the simulation.
Input Arguments
|
Estimation data. For time-domain estimation, You can estimate only discrete-time models using time-domain data. For estimating continuous-time models using time-domain data, see For frequency-domain estimation,
|
|
Order of the polynomialA(q).
|
|
Order of the polynomialB(q) + 1.
|
|
Order of the polynomialC(q).
|
|
Order of the polynomialD(q).
|
|
Order of the polynomialF(q).
|
|
Input delay in number of samples, expressed as fixed leading zeros of theBpolynomial.
|
|
Estimation options.
|
|
Linear system that configures the initial parameterization of You obtain If Use the
If If |
Name-Value Arguments
Specify optional pairs of arguments asName1=Value1,...,NameN=ValueN
, whereName
is the argument name andValue
相应的价值。名称-值参数must appear after other arguments, but the order of the pairs does not matter.
R2021a之前,用逗号来分隔每一个名字d value, and encloseName
in quotes.
|
Transport delays. For continuous-time systems, specify transport delays in the time unit stored in the For a MIMO system with Default: |
|
Input delay for each input channel, specified as a scalar value or numeric vector. For continuous-time systems, specify input delays in the time unit stored in the For a system with You can also set Default:0 |
|
Logical vector specifying integrators in the noise channel.
Setting
Where, is the integrator in the noise channel,e(t). Use For example, loadiddata1z1; z1 = iddata(cumsum(z1.y),cumsum(z1.u),z1.Ts,'InterSample','foh'); sys = polyest(z1, [2 2 2 0 0 1],'IntegrateNoise',true); |
Output Arguments
|
Polynomial model, returned as an If
Y(s),U(s) andE(s) are the Laplace transforms of the time-domain signalsy(t),u(t) ande(t), respectively. Information about the estimation results and options used is stored in the
For more information on using |
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Estimated initial conditions, returned as an
If opt = polyestOptions('InitialCondition','estimate') [sys,ic] = polyest(data,[nb nc nd nf nk],opt) 'auto' setting of'InitialCondition' uses the'zero' method when the initial conditions have a negligible effect on the overall estimation-error minimization process. Specifying'estimate' ensures that the software estimates values foric .For more information, see |
Examples
Tips
In most situations, all the polynomials of an identified polynomial model are not simultaneously active. Set one or more of the orders
na
,nc
,nd
andnf
to zero to simplify the model structure.For example, you can estimate an Output-Error (OE) model by specifying
na
,nc
andnd
as zero.Alternatively, you can use a dedicated estimating function for the simplified model structure. Linear polynomial estimation functions include
oe
,bj
,arx
andarmax
.
Alternatives
To estimate a polynomial model using time-series data, use
ar
.Use
polyest
to estimate a polynomial of arbitrary structure. If the structure of the estimated polynomial model is known, that is, you know which polynomials will be active, then use the appropriate dedicated estimating function. For examples, for an ARX model, usearx
. Other polynomial model estimating functions include,oe
,armax
, andbj
.To estimate a continuous-time transfer function, use
tfest
. You can also useoe
, but only with continuous-time frequency-domain data.