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Supported Methods for Parameter Estimation in SimBiology

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.

Method Additional Toolbox Required Supports Parameter Bounds 使用参数敏感性† Response-specific Error Models Fixed or Mixed Effects Supports Stochastic EM Algorithm SimBiology Function to Use
fminsearch — Yes* No Yes Fixed No sbiofitorfitproblem
scattersearch — Yes Depends on the selected local solver. Depends on the selected local solver. Fixed No
nlinfit(Statistics and Machine Learning Toolbox) Statistics and Machine Learning Toolbox™ Yes* No No Fixed No
fminunc(Optimization Toolbox) Optimization Toolbox™ Yes* Yes Yes Fixed No
fmincon(Optimization Toolbox) Optimization Toolbox Yes Yes Yes Fixed No
lsqcurvefit(Optimization Toolbox) Optimization Toolbox Yes Yes Yes Fixed No
lsqnonlin(Optimization Toolbox) Optimization Toolbox Yes Yes Yes Fixed No
patternsearch(Global Optimization Toolbox) Global Optimization Toolbox Yes No Yes Fixed No
ga(Global Optimization Toolbox) Global Optimization Toolbox Yes No Yes Fixed No
particleswarm(Global Optimization Toolbox) Global Optimization Toolbox Yes No Yes Fixed No
nlmefit(Statistics and Machine Learning Toolbox) Statistics and Machine Learning Toolbox No No No Mixed No sbiofitmixedorfitproblem
nlmefitsa(Statistics and Machine Learning Toolbox) Statistics and Machine Learning Toolbox No No No Mixed Yes

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

* When usingfminsearch,nlinfit, orfminuncwith bounds, the objective function returnsInfif bounds are exceeded. When you turn on options such asFunValCheck, the optimization may error if bounds are exceeded during estimation. If usingnlinfit, 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.

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