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Supported Methods for Parameter Estimation

SimBiology® supports a variety of optimization methods for least-squares and mixed-effects estimation problems. Depending on the optimization method, you can specify parameter bounds for estimated parameters as well as response-specific error models, that is, an error model for each response variable. The following table summarizes the supported optimization methods in SimBiology, fitting options, and the corresponding toolboxes that are required in addition to MATLAB® and SimBiology.

MethodAdditional Toolbox RequiredSupports Parameter BoundsUses Parameter SensitivitiesResponse-specific Error ModelsFixed or Mixed EffectsSupports Stochastic EM AlgorithmSimBiology Function to Use
fminsearchYes*NoYesFixedNosbiofit
scattersearchYesDepends on the selected local solver.Depends on the selected local solver.FixedNo
nlinfit Statistics and Machine Learning Toolbox™ Yes*NoNoFixedNo
fminunc Optimization Toolbox™ Yes*YesYesFixedNo
fmincon Optimization Toolbox YesYesYesFixedNo
lsqcurvefit Optimization Toolbox YesYesYesFixedNo
lsqnonlin Optimization Toolbox YesYesYesFixedNo
patternsearch Global Optimization Toolbox YesNoYesFixedNo
ga Global Optimization Toolbox YesNoYesFixedNo
particleswarm Global Optimization Toolbox YesNoYesFixedNo
nlmefit Statistics and Machine Learning Toolbox NoNoNoMixedNosbiofitmixed
nlmefitsa Statistics and Machine Learning Toolbox NoNoNoMixedYes

This column indicates whether the algorithm allows using parameter sensitivities to determine gradients of the objective function.

* When using fminsearch, nlinfit, or fminunc with bounds, the objective function returns Inf if bounds are exceeded. When you turn on options such as FunValCheck, the optimization may error if bounds are exceeded during estimation. If using nlinfit, it may report warnings about the Jacobian being ill-conditioned or not being able to estimate if the final result is too close to the bounds.

See Also

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