Global sensitivity and uncertainty analysis (GSUA)
- In the case of a Simulink Model, the file has some simple special structure: use a "To Workspace" connected to the output with sample time, variable name "yout" and "Structure with time" format. The parameters of models are defined with variable x. The Simulink model uses a lot of more computational time, but is the prefered method for hybrid dynamical systems (systems with continuous and discrete subsystems) and control systems.
- In the case of a m-file, specify a model using the ode45 function (see examples for file configuration). This is the preferred format.
- The model must give bounded outputs (if the simulation stops before the final time, the toolbox does not work properly).
- (1xNp) matrix
- Example (without brackets or commas): m l g x(0)
- (2xNp) matrix with nominal values in the first row and uncertainty percent in the second row. If the nominal value is a zero (x' = ax, a = 0 ± da), it is necessary to do a transformation: x' = (1 - a)x, a = 1 ± da.
- Example: [1.5 4.3 25; 2.4 0.8 13]. For: x1 = 1.5 (2.4%), x2 = 4.3 (0.8%), x3 = 25 (13%)
- Dynamical case: time response for every family of factors (the nominal or experimental time output is highlighted).
- Static case (function): histogram plot which shows how the output varies with changes on factors
- Dynamical case: vectorial first-order sensitivity indices which depend on time
- Static case (function): scalar first-order sensitivity indices for the scalar function value
- The first-order indices must be positive and their sum should be less than one, so if these conditions are not met, it is necessary to increase sample size N.
- Dynamical case: vectorial total sensitivity indices which depend on time
- Static case (function): scalar total sensitivity indices for the scalar function value
- OAT sensitivity method: this plot does not apply
- Dynamical case: scalar first-order sensitivity indices for the scalar characteristic of time response using pie or bar plots
- Static case (function): scalar first-order sensitivity indices for the squared error
- The first-order indices must be positive and their sum should be less than one, so if these conditions are not met, it is necessary to increase sample size N.
- Dynamical case: scalar total sensitivity indices for the scalar characteristic of time response using pie or bar plots
- Static case (function): scalar total sensitivity indices for the squared error
- OAT sensitivity method: this plot does not apply
- Scatter plot for scalar output, where Ys is a function of factors. Look for correlations and relative values.
Cite As
Carlos M. Velez S. (2024). Global sensitivity and uncertainty analysis (GSUA) (https://www.mathworks.com/matlabcentral/fileexchange/47758-global-sensitivity-and-uncertainty-analysis-gsua), MATLAB Central File Exchange. Retrieved .
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GSUA
GSUA/Examples
GSUA/Examples
GSUA
GSUA/Examples
Version | Published | Release Notes | |
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4.6 | - Better user interface
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4.5 | It is easier to give the Simulink model in the right form: use a "To Workspace" connected to the output with sample time, Variable name "yout" and "Structure with time" format. |
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4.1 | - Minor changes
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4.0 | - Simplified environment
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3.0 | (1) A main script is included to better application of toolbox. (2) A user manual is included. |
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2.8.0.0 | All functions were optimized in three functions. Bar plots were included. All plots were improved. Sensitivity indices are shown for temporal responses and for scalar minimum square error (MSE) function. The estimated processing time is displayed. |
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2.1.0.0 | The remaining time is displayed. |
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2.0.0.0 | Other sensitivity methods are included (Sobol, Jansen, Saltelli).
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1.4.0.0 | Integration as a toolbox. |
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1.3.0.0 | New functions and examples are included. |
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1.2.0.0 | Correction of pendulum example. |
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1.1.0.0 | Correction of function description. |
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1.0.0.0 |