nlhwOptions

Option set for nlhw

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Syntax

opt = nlhwOptions
opt = nlhwOptions(Name,Value)

Description

example

opt = nlhwOptions creates the default option set for nlhw. Use dot notation to customize the option set, if needed.

example

opt = nlhwOptions(Name,Value) creates an option set with options specified by one or more Name,Value pair arguments. The options that you do not specify retain their default value.

Examples

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Open Live Script

Create estimation option set for nlhw to view estimation progress and to set the maximum iteration steps to 50. 

opt = nlhwOptions;
opt.Display = 'on';
opt.SearchOptions.MaxIterations = 50;

Load data and estimate the model.

load iddata3
sys = nlhw(z3,[4 2 1],'sigmoidnet','deadzone',opt);
Open Live Script

Create an options set for nlhw where:

  • Initial conditions are estimated from the estimation data.

  • Subspace Gauss-Newton least squares method is used for estimation. 

opt = nlhwOptions('InitialCondition','estimate','SearchMethod','gn');

Input Arguments

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Name-Value Pair Arguments

Specify optional comma-separated 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.

Example: nlhwOptions('InitialCondition','estimate')

Handling of initial conditions during estimation using nlhw, specified as the comma-separated pair consisting of InitialCondition and one of the following:

  • 'zero' — The initial conditions are set to zero.

  • 'estimate' — The initial conditions are treated as independent estimation parameters.

Estimation progress display setting, specified as the comma-separated pair consisting of 'Display' and one of the following:

  • 'off' — No progress or results information is displayed.

  • 'on' — Information on model structure and estimation results are displayed in a progress-viewer window.

Weighting of prediction error in multi-output model estimations, specified as the comma-separated pair consisting of 'OutputWeight' and one of the following:

  • 'noise' — Optimal weighting is automatically computed as the inverse of the estimated noise variance. This weighting minimizes det(E'*E), where E is the matrix of prediction errors. This option is not available when using 'lsqnonlin' as a 'SearchMethod'.

  • A positive semidefinite matrix, W, of size equal to the number of outputs. This weighting minimizes trace(E'*E*W/N), where E is the matrix of prediction errors and N is the number of data samples.

Options for regularized estimation of model parameters, specified as the comma-separated pair consisting of 'Regularization' and a structure with fields:

Field NameDescriptionDefault
LambdaBias versus variance trade-off constant, specified as a nonnegative scalar.0 — Indicates no regularization.
RWeighting matrix, specified as a vector of nonnegative scalars or a square positive semi-definite matrix. The length must be equal to the number of free parameters in the model, np. Use the nparams command to determine the number of model parameters.1 — Indicates a value of eye(np).
Nominal

The nominal value towards which the free parameters are pulled during estimation, specified as one of the following:

  • 'zero' — Pull parameters towards zero.

  • 'model' — Pull parameters towards pre-existing values in the initial model. Use this option only when you have a well-initialized idnlhw model with finite parameter values.

'zero'

To specify field values in Regularization, create a default nlhwOptions set and modify the fields using dot notation. Any fields that you do not modify retain their default values.

opt = nlhwOptions;
opt.Regularization.Lambda = 1.2;
opt.Regularization.R = 0.5*eye(np);

Regularization is a technique for specifying model flexibility constraints, which reduce uncertainty in the estimated parameter values. For more information, see Regularized Estimates of Model Parameters.

Numerical search method used for iterative parameter estimation, specified as the comma-separated 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 Gauss-Newton least squares search. Singular values of the Jacobian matrix less than GnPinvConstant*eps*max(size(J))*norm(J) are discarded when computing the search direction. J is the Jacobian matrix. The Hessian matrix is approximated as JTJ. If there is no improvement in this direction, the function tries the gradient direction.

  • 'gna' — Adaptive subspace Gauss-Newton search. Eigenvalues less than gamma*max(sv) of the Hessian are ignored, where sv contains the singular values of the Hessian. The Gauss-Newton direction is computed in the remaining subspace. gamma has the initial value InitialGnaTolerance (see Advanced in 'SearchOptions' for more information). This value is increased by the factor LMStep each time the search fails to find a lower value of the criterion in fewer than five bisections. This value is decreased by the factor 2*LMStep each time a search is successful without any bisections.

  • 'lm' — Levenberg-Marquardt least squares search, where the next parameter value is -pinv(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' — Trust-region-reflective algorithm of lsqnonlin (Optimization Toolbox). Requires Optimization Toolbox™ software.

  • 'fmincon' — Constrained nonlinear solvers. You can use the sequential quadratic programming (SQP) and trust-region-reflective algorithms of the fmincon (Optimization Toolbox) solver. If you have Optimization Toolbox software, you can also use the interior-point and active-set algorithms of the fmincon solver. Specify the algorithm in the SearchOptions.Algorithm option. The fmincon 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.

    • Multi-output model estimation. A determinant loss function is minimized by default for multi-output 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, the fmincon algorithms can offer better efficiency and accuracy for multi-output model estimations.

Option set for the search algorithm, specified as the comma-separated 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 NameDescriptionDefault
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 Tolerance, the iterations stop. The estimate of the expected loss-function improvement at the next iteration is based on the Gauss-Newton vector computed for the current parameter value.

1e-5
MaxIterations

Maximum number of iterations during loss-function minimization, specified as a positive integer. The iterations stop when MaxIterations is reached or another stopping criterion is satisfied, such as Tolerance.

Setting MaxIterations = 0 returns the result of the start-up procedure.

Use sys.Report.Termination.Iterations to get the actual number of iterations during an estimation, where sys is an idtf model.

20
Advanced

Advanced search settings, specified as a structure with the following fields:

Field NameDescriptionDefault
GnPinvConstant

Jacobian matrix singular value threshold, specified as a positive scalar. Singular values of the Jacobian matrix that are smaller than GnPinvConstant*max(size(J)*norm(J)*eps) are discarded when computing the search direction. Applicable when SearchMethod is 'gn'.

10000
InitialGnaTolerance

Initial value of gamma, specified as a positive scalar. Applicable when SearchMethod is 'gna'.

0.0001
LMStartValue

Starting value of search-direction length d in the Levenberg-Marquardt method, specified as a positive scalar. Applicable when SearchMethod is 'lm'.

0.001
LMStep

Size of the Levenberg-Marquardt step, specified as a positive integer. The next value of the search-direction length d in the Levenberg-Marquardt method is LMStep times the previous one. Applicable when SearchMethod is 'lm'.

2
MaxBisections

Maximum number of bisections used for line search along the search direction, specified as a positive integer.

25
MaxFunctionEvaluations

Maximum number of calls to the model file, specified as a positive integer. Iterations stop if the number of calls to the model file exceeds this value.

Inf
MinParameterChange

Smallest parameter update allowed per iteration, specified as a nonnegative scalar.

0
RelativeImprovement

Relative improvement threshold, specified as a nonnegative scalar. Iterations stop if the relative improvement of the criterion function is less than this value.

0
StepReduction

Step reduction factor, specified as a positive scalar that is greater than 1. The suggested parameter update is reduced by the factor StepReduction after each try. This reduction continues until MaxBisections tries are completed or a lower value of the criterion function is obtained.

StepReduction is not applicable for SearchMethod 'lm' (Levenberg-Marquardt method).

2

SearchOptions Structure When SearchMethod is Specified as 'lsqnonlin'

Field NameDescriptionDefault
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 FunctionTolerance is the same as that of opt.SearchOptions.Advanced.TolFun.

1e-5
StepTolerance

Termination tolerance on the estimated parameter values, specified as a positive scalar.

The value of StepTolerance is the same as that of opt.SearchOptions.Advanced.TolX.

1e-6
MaxIterations

Maximum number of iterations during loss-function minimization, specified as a positive integer. The iterations stop when MaxIterations is reached or another stopping criterion is satisfied, such as FunctionTolerance.

The value of MaxIterations is the same as that of opt.SearchOptions.Advanced.MaxIter.

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Advanced

Advanced search settings, specified as an option set for lsqnonlin.

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 NameDescriptionDefault
Algorithm

fmincon optimization algorithm, specified as one of the following:

  • 'sqp' — Sequential quadratic programming algorithm. The algorithm satisfies bounds at all iterations, and it can recover from NaN or Inf results. It is not a large-scale algorithm. For more information, see Large-Scale vs. Medium-Scale Algorithms (Optimization Toolbox).

  • 'trust-region-reflective' — Subspace trust-region method based on the interior-reflective Newton method. It is a large-scale algorithm.

  • 'interior-point' — Large-scale algorithm that requires Optimization Toolbox software. The algorithm satisfies bounds at all iterations, and it can recover from NaN or Inf results.

  • 'active-set' — Requires Optimization Toolbox software. The algorithm can take large steps, which adds speed. It is not a large-scale 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.

1e-6
StepTolerance

Termination tolerance on the estimated parameter values, specified as a positive scalar.

1e-6
MaxIterations

Maximum number of iterations during loss function minimization, specified as a positive integer. The iterations stop when MaxIterations is reached or another stopping criterion is satisfied, such as FunctionTolerance.

100

To specify field values in SearchOptions, create a default nlhwOptions set and modify the fields using dot notation. Any fields that you do not modify retain their default values.

opt = nlhwOptions;
opt.SearchOptions.MaxIterations = 50;
opt.SearchOptions.Advanced.RelImprovement = 0.5;

Additional advanced options, specified as the comma-separated pair consisting of 'Advanced' and a structure with fields:

Field NameDescriptionDefault
ErrorThresholdThreshold for when to adjust the weight of large errors from quadratic to linear, specified as a nonnegative scalar. Errors larger than ErrorThreshold times the estimated standard deviation have a linear weight in the loss function. The standard deviation is estimated robustly as the median of the absolute deviations from the median of the prediction errors, divided by 0.7. If your estimation data contains outliers, try setting ErrorThreshold to 1.6.0 — Leads to a purely quadratic loss function.
MaxSizeMaximum number of elements in a segment when input-output data is split into segments, specified as a positive integer.250000

To specify field values in Advanced, create a default nlhwOptions set and modify the fields using dot notation. Any fields that you do not modify retain their default values.

opt = nlhwOptions;
opt.Advanced.ErrorThreshold = 1.2;

Output Arguments

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Option set for nlhw, returned as an nlhwOptions option set.

Compatibility Considerations

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See Also

Introduced in R2015a

System Identification Toolbox Documentation

Support