findopOptions
Option set for findop
Description
opt = findopOptions(
creates
a default option set for computing the operating point of a specified
nonlinear ARX or HammersteinWiener model. Use dot notation to modify
this option set for your specific application. Options that you do
not modify retain their default values.model
)
creates
an option set with options specified by one or more opt
= findopOptions(model
,Name,Value
)Name,Value
pair
arguments.
Examples
Create Default Option Set for Operating Point Search
Create a default option set for findop
using an idnlarx
model
opt = findopOptions(idnlarx);
Create and Modify Default Operating Point Search Options
Create a default option set for findop
using an idnlhw
model.
opt = findopOptions(idnlhw);
Use dot notation to specify a subspace GaussNewton least squares search with a maximum of 25 iterations.
opt.SearchMethod = 'gn';
opt.SearchOptions.MaxIterations = 25;
Specify Options for Operating Point Search
Create an option set for findop
using an idnlarx
model. Specify a steepest descent least squares search with default search options.
opt = findopOptions(idnlarx,'SearchMethod','grad');
Input Arguments
model
— Estimated nonlinear model
idnlarx
model  idnlhw
model
Estimated nonlinear model, specified as one of the following:
idnlarx
modelidnlhw
model
NameValue Arguments
Specify optional
commaseparated pairs of Name,Value
arguments. Name
is
the argument name and Value
is the corresponding value.
Name
must appear inside quotes. You can specify several name and value
pair arguments in any order as
Name1,Value1,...,NameN,ValueN
.
'SearchMethod','grad'
specifies a
steepest descent least squares search methodSearchMethod
— Numerical search method used for iterative parameter estimation
'auto'
(default)  'gn'
 'gna'
 'lm'
 'grad'
 'lsqnonlin'
 'fmincon'
Numerical search method used for iterative parameter estimation,
specified as the commaseparated pair consisting of 'SearchMethod'
and
one of the following:
'auto'
— A combination of the line search algorithms,'gn'
,'lm'
,'gna'
, and'grad'
methods is tried in sequence at each iteration. The first descent direction leading to a reduction in estimation cost is used.'gn'
— Subspace GaussNewton least squares search. Singular values of the Jacobian matrix less thanGnPinvConstant*eps*max(size(J))*norm(J)
are discarded when computing the search direction. J is the Jacobian matrix. The Hessian matrix is approximated as J^{T}J. If there is no improvement in this direction, the function tries the gradient direction.'gna'
— Adaptive subspace GaussNewton search. Eigenvalues less thangamma*max(sv)
of the Hessian are ignored, where sv contains the singular values of the Hessian. The GaussNewton direction is computed in the remaining subspace. gamma has the initial valueInitialGnaTolerance
(seeAdvanced
in'SearchOptions'
for more information). This value is increased by the factorLMStep
each time the search fails to find a lower value of the criterion in fewer than five bisections. This value is decreased by the factor2*LMStep
each time a search is successful without any bisections.'lm'
— LevenbergMarquardt least squares search, where the next parameter value ispinv(H+d*I)*grad
from the previous one. H is the Hessian, I is the identity matrix, and grad is the gradient. d is a number that is increased until a lower value of the criterion is found.'grad'
— Steepest descent least squares search.'lsqnonlin'
— Trustregionreflective algorithm oflsqnonlin
(Optimization Toolbox). Requires Optimization Toolbox™ software.'fmincon'
— Constrained nonlinear solvers. You can use the sequential quadratic programming (SQP) and trustregionreflective algorithms of thefmincon
(Optimization Toolbox) solver. If you have Optimization Toolbox software, you can also use the interiorpoint and activeset algorithms of thefmincon
solver. Specify the algorithm in theSearchOptions.Algorithm
option. Thefmincon
algorithms may result in improved estimation results in the following scenarios:Constrained minimization problems when there are bounds imposed on the model parameters.
Model structures where the loss function is a nonlinear or non smooth function of the parameters.
Multioutput model estimation. A determinant loss function is minimized by default for multioutput model estimation.
fmincon
algorithms are able to minimize such loss functions directly. The other search methods such as'lm'
and'gn'
minimize the determinant loss function by alternately estimating the noise variance and reducing the loss value for a given noise variance value. Hence, thefmincon
algorithms can offer better efficiency and accuracy for multioutput model estimations.
SearchOptions
— Option set for the search algorithm
search option set
Option set for the search algorithm, specified as the commaseparated
pair consisting of 'SearchOptions'
and a search
option set with fields that depend on the value of
SearchMethod
.
SearchOptions
Structure When SearchMethod
is Specified
as 'gn'
, 'gna'
, 'lm'
,
'grad'
, or 'auto'
Field Name  Description  Default  

Tolerance  Minimum percentage difference between the current value
of the loss function and its expected improvement after the next iteration,
specified as a positive scalar. When the percentage of expected improvement
is less than  0.01  
MaxIterations  Maximum number of iterations during lossfunction minimization, specified as a positive
integer. The iterations stop when Setting
Use
 20  
Advanced  Advanced search settings, specified as a structure with the following fields:

SearchOptions
Structure When SearchMethod
is Specified
as 'lsqnonlin'
Field Name  Description  Default 

FunctionTolerance  Termination tolerance on the loss function that the software minimizes to determine the estimated parameter values, specified as a positive scalar. The
value of  1e5 
StepTolerance  Termination tolerance on the estimated parameter values, specified as a positive scalar. The value of  1e6 
MaxIterations  Maximum number of iterations during lossfunction minimization, specified as a positive
integer. The iterations stop when The value of
 20 
Advanced  Advanced search settings, specified as an option set
for For more information, see the Optimization Options table in Optimization Options (Optimization Toolbox).  Use optimset('lsqnonlin') to create a default
option set. 
SearchOptions
Structure When SearchMethod
is Specified
as 'fmincon'
Field Name  Description  Default 

Algorithm 
For more information about the algorithms, see Constrained Nonlinear Optimization Algorithms (Optimization Toolbox) and Choosing the Algorithm (Optimization Toolbox).  'sqp' 
FunctionTolerance  Termination tolerance on the loss function that the software minimizes to determine the estimated parameter values, specified as a positive scalar.  1e6 
StepTolerance  Termination tolerance on the estimated parameter values, specified as a positive scalar.  1e6 
MaxIterations  Maximum number of iterations during loss function minimization, specified as a positive
integer. The iterations stop when  100 
To specify field values in SearchOptions
, create a
default findopOptions
set and modify the fields using
dot notation. Any fields that you do not modify retain their default
values.
opt = findopOptions; opt.SearchOptions.MaxIterations = 15; opt.SearchOptions.Advanced.RelImprovement = 0.5;
Output Arguments
opt
— Option set for findop
command
findopOptions
object
Option set for findop
command, returned
as a findopOptions
object.
Compatibility Considerations
Renaming of Estimation and Analysis Options
The names of some estimation and analysis options were changed in R2018a. Prior names still work. For details, see the R2018a release note Renaming of Estimation and Analysis Options.
See Also
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