Main Content


グレーボックス モデルの推定



greyestLinear grey-box model estimation
nlgreyestEstimate nonlinear grey-box model parameters
idgreyLinear ODE (grey-box model) with identifiable parameters
idnlgreyNonlinear grey-box model
pemPrediction error minimization for refining linear and nonlinear models
findstatesEstimate initial states of model
initSet or randomize initial parameter values
getinitidnlgrey モデルの初期状態の値
setinitidnlgrey モデル オブジェクトの初期状態を設定する
getparidnlgrey モデル パラメーターのパラメーター値とプロパティ
setparidnlgrey モデル オブジェクトの初期パラメーター値を設定する
getpvecObtain model parameters and associated uncertainty data
setpvecModify values of model parameters
simSimulate response of identified model
greyestOptionsOption set for greyest
nlgreyestOptionsOption set for nlgreyest
findstatesOptionsOption set for findstates
simOptionsOption set for sim


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 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



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 idgrey and idnlgrey model 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 Report property 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.