Use Sensitivity Analysis to evaluate how the parameters and states of a Simulink® model influence the model output or model design requirements. You can evaluate your model in the Sensitivity Analyzer, or at the command line. You can speed up the evaluation using parallel computing or fast restart. In the Sensitivity Analyzer, after performing sensitivity analysis, you can export the analysis results to the Parameter Estimator or Response Optimizer apps. To learn more about sensitivity analysis and its applications, see What is Sensitivity Analysis?
|Sensitivity Analyzer||Explore design space and determine most influential model parameters|
|Simulation scenario description|
|Piecewise-linear amplitude bound|
|Reference signal to track|
|Step response bound on signal|
|Impose elliptic bound on phase plane trajectory of two signals|
|Impose region bound on phase plane trajectory of two signals|
|Impose function matching constraint on variable|
|Impose monotonic constraint on variable|
|Impose relational constraint on pair of variables|
|Impose bounds on gradient magnitude of variable|
|Bode magnitude bound|
|Closed loop peak gain bound|
|Gain and phase margin bounds|
|Nichols response bound|
|Damping ratio bound|
|Natural frequency bound|
|Settling time bound|
|Singular value bound|
|Evaluate cost function for samples|
|Cost function evaluation
options for |
|Set up steady-state operating point computation|
|Design variable for optimization|
|Initial state for estimation from Simulink model|
|List of model file and path dependencies|
|Set design variable value in model|
Simulink Design Optimization™ software performs global sensitivity analysis.
This topic shows how to generate parameter samples for sensitivity analysis.
Use visual and statistical analysis techniques to analyze the relationship between the parameters and design requirements.
Validate sensitivity analysis by checking generated parameter values, evaluation results, and analysis results.
Write a cost function for parameter estimation, response optimization, or sensitivity analysis. The cost function evaluates your design requirements using design variable values.
This example shows how to use sensitivity analysis to narrow down the number of parameters that you need to estimate when fitting a model.
This example shows how to use sensitivity analysis to narrow down the number of parameters that you need to estimate to fit a model.
This example shows how to sample and explore a design space using the Sensitivity Analyzer.
This example shows how to sample and explore a design space.
This example shows how to use the Sensitivity Analyzer to explore the behavior of a PI controller for a DC motor.
An operating point of a dynamic system defines the states and root-level input signals of the model at a specific time.
Specify model dependencies and use parallel computing for performing sensitivity analysis in the app, or at the command line.
This topic shows how to speed up sensitivity analysis using Simulink fast restart.
Design Optimization software supports
Accelerator simulation modes.
How to speed up evaluation in the app by storing intermediate data.
Select model parameters for sensitivity analysis in the app.
Specify time-domain requirements such as signal matching, amplitude bounds, step response bounds, reference signals, elliptical bounds, and custom bounds.
Specify requirements such as monotonic, smoothness, property, and relational constraints on parameters in your model.
Specify frequency-domain requirements in the Sensitivity Analyzer.
Perform preprocessing operations such as removing offsets and filtering the data before you use it.
Create linearization input/output sets in the Response Optimizer or Sensitivity Analyzer.
Evaluate your design requirements in the Sensitivity Analyzer.
Use the results generated in the Sensitivity Analyzer to configure parameter estimation or response optimization.
Plot and interpret parameter set, requirement, result scatter, contour, and tornado plots.
This example shows how to automatically generate a MATLAB function to solve a Sensitivity Analysis evaluation problem.
This example shows how to automatically generate a MATLAB function to solve a Sensitivity Analysis statistics problem.