Hammerstein-Wiener model
sys = idnlhw(Orders)
sys = idnlhw(Orders,InputNL,OutputNL)
sys = idnlhw(Orders,InputNL,OutputNL,Name,Value)
sys = idnlhw(LinModel)
sys = idnlhw(LinModel,InputNL,OutputNL)
sys = idnlhw(LinModel,InputNL,OutputNL,Name,Value)
creates
a Hammerstein-Wiener model with the specified orders, and using piecewise
linear functions as input and output nonlinearity estimators.sys
= idnlhw(Orders
)
uses sys
= idnlhw(Orders
,InputNL
,OutputNL
)InputNL
and OutputNL
as
the input and output nonlinearity estimators, respectively.
specifies additional attributes of the sys
= idnlhw(Orders
,InputNL
,OutputNL
,Name,Value
)idnlhw
model
structure using one or more Name,Value
pair arguments.
uses a linear model sys
= idnlhw(LinModel
)LinModel
to specify the model
orders and default piecewise linear functions for the input and output
nonlinearity estimators.
specifies
input and output nonlinearity estimators for the model.sys
= idnlhw(LinModel
,InputNL
,OutputNL
)
specifies additional attributes of the sys
= idnlhw(LinModel
,InputNL
,OutputNL
,Name,Value
)idnlhw
model
structure using one or more Name,Value
pair arguments.
idnlhw
represents a Hammerstein-Wiener
model. The Hammerstein-Wiener structure represents
a linear model with input-output nonlinearities.
Use the nlhw
command to
both construct an idnlhw
object and estimate
the model parameters.
You can also use the idnlhw
constructor
to create the Hammerstein-Wiener model and then estimate the model
parameters using nlhw
.
For idnlhw
object properties, see Properties.
idnlhw
object properties include:
|
Model orders and delays of the linear subsystem transfer function,
where For a MIMO transfer function with | ||||||||||||||||
|
B polynomial of the linear block in the model
structure, specified as a cell array of | ||||||||||||||||
|
F polynomial of the linear block in the model
structure, specified as a cell array of | ||||||||||||||||
|
Input nonlinearity estimator, specified as one of the following:
Specifying a character vector creates a nonlinearity estimator object with default settings. Use object representation instead to configure the properties of a nonlinearity estimator. InputNonlinearity = wavenet; InputNonlinearity.NumberOfUnits = 10; Alternatively, use the associated input nonlinearity estimator function with Name-Value pair arguments. InputNonlinearity = wavenet('NumberOfUnits',10); For Default: | ||||||||||||||||
|
Output nonlinearity estimator, specified as one of the following:
Specifying a character vector creates a nonlinearity estimator object with default settings. Use object representation instead to configure the properties of a nonlinearity estimator. OutputNonlinearity = sigmoidnet; OutputNonlinearity.NumberOfUnits = 10; Alternatively, use the associated input nonlinearity estimator function with Name-Value pair arguments. OutputNonlinearity = sigmoidnet('NumberOfUnits',10); For Default: | ||||||||||||||||
|
The linear model in the linear block of the model structure,
represented as an | ||||||||||||||||
|
Summary report that contains information about the estimation
options and results when the model is estimated using the
The contents of m = idnlhw([2 2 1]); m.Report.OptionsUsed ans = [] If you use load iddata1; m = nlhw(z1,[2 2 1],[],'pwlinear'); m.Report.OptionsUsed Option set for the nlhw command: InitialCondition: 'zero' Display: 'off' Regularization: [1x1 struct] SearchMethod: 'auto' SearchOption: [1x1 idoptions.search.identsolver] OutputWeight: 'noise' Advanced: [1x1 struct]
For more information on this property and how to use it, see Output Arguments in
the | ||||||||||||||||
|
Independent variable for the inputs, outputs, and—when
available—internal states, specified as a character vector. Default: | ||||||||||||||||
|
Noise variance (covariance matrix) of the model innovations e. | ||||||||||||||||
|
Sample time. Changing this property does not discretize or resample the model. Default: | ||||||||||||||||
|
Units for the time variable, the sample time
Changing this property has no effect on other properties, and
therefore changes the overall system behavior. Use Default: | ||||||||||||||||
|
Input channel names, specified as one of the following:
Alternatively, use automatic vector expansion to assign input
names for multi-input models. For example, if sys.InputName = 'controls'; The input names automatically expand to When you estimate a model using an You can use the shorthand notation Input channel names have several uses, including:
Default: | ||||||||||||||||
|
Input channel units, specified as one of the following:
Use Default: | ||||||||||||||||
|
Input channel groups. The sys.InputGroup.controls = [1 2]; sys.InputGroup.noise = [3 5]; creates input groups named sys(:,'controls') Default: Struct with no fields | ||||||||||||||||
|
Output channel names, specified as one of the following:
Alternatively, use automatic vector expansion to assign output
names for multi-output models. For example, if sys.OutputName = 'measurements'; The output names automatically expand to When you estimate a model using an You can use the shorthand notation Output channel names have several uses, including:
Default: | ||||||||||||||||
|
Output channel units, specified as one of the following:
Use Default: | ||||||||||||||||
|
Output channel groups. The sys.OutputGroup.temperature = [1]; sys.InputGroup.measurement = [3 5]; creates output groups named sys('measurement',:) Default: Struct with no fields | ||||||||||||||||
|
System name, specified as a character vector. For example, Default: | ||||||||||||||||
|
Any text that you want to associate with the system, stored as a string or a cell array of
character vectors. The property stores whichever data type you
provide. For instance, if sys1.Notes = "sys1 has a string."; sys2.Notes = 'sys2 has a character vector.'; sys1.Notes sys2.Notes ans = "sys1 has a string." ans = 'sys2 has a character vector.' Default: | ||||||||||||||||
|
Any type of data you want to associate with system, specified as any MATLAB® data type. Default: |
customnet
| findop
| linear
| linearize
| nlhw
| pem
| poly1d
| saturation
| saturation
| sigmoidnet
| wavenet