|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. The code in this example uses the Control System Toolbox™ command
kalman (Control System Toolbox) for computing the Kalman gain
from the known and estimated noise variance.
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. The example requires Control System Toolbox™ software.
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 approach is useful when you can derive state-space matrices from physical principles and provide initial parameter values based on physical insight. You can use this approach to discover what happens if you fix specific parameter values or if you free certain parameters.
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. The input signals are measured while the noise signals are only known via their statistical mean and variance. The general form of the state-space model, often associated with Kalman filtering, is an example of such a model, and is defined as:
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. This report is stored in the
property of the estimated model. The exact contents of the report depend on the estimator
function you use to obtain the model.
Regularization is the technique for specifying constraints on the flexibility of a model, thereby reducing uncertainty in the estimated parameter values.