|Linear grey-box model estimation|
|Linear ODE (grey-box model) with identifiable parameters|
|Prediction error estimate for linear and nonlinear model|
|Estimate initial states of model|
|Set or randomize initial parameter values|
|Model parameters and associated uncertainty data|
|Modify value of model parameters|
|Obtain attributes such as values and bounds of linear model parameters|
|Set attributes such as values and bounds of linear model parameters|
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.
Structured parameterization lets you exclude specific parameters from estimation by setting these parameters to specific values.
This example shows how to 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.
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.