|Linear grey-box model estimation|
|Estimate nonlinear grey-box model parameters|
|Linear ODE (grey-box model) with identifiable parameters|
|Nonlinear grey-box model|
|Prediction error estimate for linear and nonlinear model|
|Estimate initial states of model|
|Set or randomize initial parameter values|
|Values of |
|Set initial states of |
|Parameter values and properties of |
|Set initial parameter values of |
|Model parameters and associated uncertainty data|
|Modify value of model parameters|
|Simulate response of identified model|
How to define and estimate linear grey-box models at the command line.
This example shows how to estimate the heat conductivity and the heat-transfer coefficient of a continuous-time grey-box model for a heated-rod system.
This example shows how to create a single-input and single-output grey-box model structure when you know the variance of the measurement noise.
Estimate model parameters using linear and nonlinear grey-box modeling.
This example shows how to estimate a model that is parameterized by poles, zeros, and gains.
How to define and estimate nonlinear grey-box models at the command line.
This example shows how to write ODE files for nonlinear grey-box models as MATLAB and C MEX files.
Structured parameterization lets you exclude specific parameters from estimation by setting these parameters to specific values.
This example shows how to estimate parameters in user-defined model structures.
Types of supported grey-box models.
Types of supported data for estimating grey-box models.
objects for representing grey-box model objects.
An identified linear model is used to simulate and predict system outputs for given input and noise signals.
Configure the loss function that is minimized during parameter estimation. After estimation, use model quality metrics to assess the quality of identified models.
The estimation report contains information about the results and options used for a model estimation.
Regularization is the technique for specifying constraints on the flexibility of a model, thereby reducing uncertainty in the estimated parameter values.