線形グレー ボックス モデル
|Linear grey-box model estimation|
|Linear ODE (grey-box model) with identifiable parameters|
|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|
- 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. 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 State-Space Models with Structured Parameterization
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.
- 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. The example requires Control System Toolbox™ software.
- 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. 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: