グレーボックス モデルの推定
関数
greyest | Estimate ODE parameters of linear grey-box model |
nlgreyest | Estimate nonlinear grey-box model parameters |
idgrey | Linear ODE (grey-box model) with identifiable parameters |
idnlgrey | Nonlinear grey-box model |
pem | 線形および非線形のモデルを改良するための予測誤差の最小化 |
findstates | Estimate initial states of model |
init | 初期パラメーター値を設定またはランダム化する |
getinit | idnlgrey モデルの初期状態の値 |
setinit | idnlgrey モデル オブジェクトの初期状態を設定する |
getpar | idnlgrey モデル パラメーターのパラメーター値とプロパティ |
setpar | idnlgrey モデル オブジェクトの初期パラメーター値を設定する |
getpvec | モデル パラメーターと、関連する不確かさのデータを取得する |
setpvec | Modify values of model parameters |
sim | 同定されたモデルの応答のシミュレーション |
greyestOptions | Option set for greyest |
nlgreyestOptions | Option set for nlgreyest |
findstatesOptions | Option set for findstates |
simOptions | Option set for sim |
例および使用方法
- 線形グレーボックス モデルの推定
コマンド ラインで線形グレーボックス モデルを定義および推定する方法。
- 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 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.
- Estimate Nonlinear Grey-Box Models
How to define and estimate nonlinear grey-box models at the command line.
- IDNLGREY モデル ファイルの作成
この例では、非線形グレー ボックス モデルの ODE ファイルを MATLAB® ファイルおよび C MEX ファイルとして作成する方法を説明します。
- 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.
- System Identification Toolbox を使用した構造化されたユーザー定義モデルの構築
この例では、ユーザー定義モデル構造でパラメーターを推定する方法を説明します。
概念
- 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
Difference between
idgreyandidnlgreymodel 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:
- Loss Function and Model Quality Metrics
Configure the loss function that is minimized during parameter estimation. After estimation, use model quality metrics to assess the quality of identified models.
- Estimation Report
The estimation report contains information about the results and options used for a model estimation. This report is stored in the
Reportproperty of the estimated model. The exact contents of the report depend on the estimator function you use to obtain the model. - Regularized Estimates of Model Parameters
Regularization is the technique for specifying constraints on the flexibility of a model, thereby reducing uncertainty in the estimated parameter values.
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