Linear Grey-Box Models
|Linear grey-box model estimation|
|Linear ODE (grey-box model) with identifiable parameters|
|Prediction error minimization for refining linear and nonlinear models|
|Estimate initial states of model|
|Set or randomize initial parameter values|
|Obtain model parameters and associated uncertainty data|
|Modify values 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|
Examples and How To
- Estimate Linear Grey-Box Models
How to define and estimate linear grey-box models at the command line.
- Estimate Continuous-Time Grey-Box Model for Heat Diffusion
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.
- Estimate Discrete-Time Grey-Box Model with Parameterized Disturbance
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 State-Space Models with Structured Parameterization
Structured parameterization lets you exclude specific parameters from estimation by setting these parameters to specific values.
- Estimate Coefficients of ODEs to Fit Given Solution
Estimate model parameters using linear and nonlinear grey-box modeling.
- Estimate Model Using Zero/Pole/Gain Parameters
This example shows how to estimate a model that is parameterized by poles, zeros, and gains.
- Supported Grey-Box Models
Types of supported grey-box models.
- Data Supported by Grey-Box Models
Types of supported data for estimating grey-box models.
- Choosing idgrey or idnlgrey Model Object
idnlgreymodel objects for representing grey-box model objects.
- Identifying State-Space Models with Separate Process and Measurement Noise Descriptions
An identified linear model is used to simulate and predict system outputs for given input and noise signals.