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線形モデル同定の基礎

線形モデルの同定、ブラックボックスのモデル化、モデル構造の選択、正則化

例および操作のヒント

Identify Linear Models Using System Identification App

Identify linear black-box models from single-input/single-output (SISO) data using the System Identification app.

Identify Linear Models Using the Command Line

Identify linear models from multiple-input/single-output (MISO) data using System Identification Toolbox™ commands.

Transfer Function Structure Specification

Specify the values and constraints for the numerator, denominator and transport delays.

Specifying Initial Conditions for Iterative Estimation of Transfer Functions

Specify how initial conditions are handled during model estimation in the app and at the command line.

モデル構造の選択: モデル次数と入力遅延の決定

この例では、モデル構造の選択と構成を行う方法をいくつか示します。

周波数領域同定: 周波数領域データを使用したモデルの推定

この例では、周波数領域データを使用してモデルを推定する方法を説明します。

動的システムの正則化による同定

この例では、線形モデルと非線形モデルを同定するときに正則化を行う利点を説明します。

Estimate Regularized ARX Model Using System Identification App

This example shows how to estimate regularized ARX models using automatically generated regularization constants in the System Identification app.

概念

Types of Model Objects

Model object types include numeric models, for representing systems with fixed coefficients, and generalized models for systems with tunable or uncertain coefficients.

About Identified Linear Models

System Identification Toolbox software uses objects to represent a variety of linear and nonlinear model structures.

Available Linear Models

Summary of linear model types that you can use for system identification.

Linear Model Structures

Linear models in System Identification Toolbox take the form of model objects that are linear model structures. You can construct model objects directly or use estimation commands to both construct and estimate models. You can also modify the properties of existing model objects.

Black-Box Modeling

Black-box modeling is useful when your primary interest is in fitting the data regardless of a particular mathematical structure of the model.

Recommended Model Estimation Sequence

Recommended model estimation sequence, from the simplest to the more complex model structures.

Imposing Constraints on Model Parameter Values

Constrain the adjustments that the estimation algorithm can make to individual model parameters by using the Structure property of the mode object.

Determining Model Order and Delay

Estimation requires you to specify the model order and delay. Many times, these values are not known.

Effect of Input Intersample Behavior on Continuous-Time Models

The intersample behavior of the input signals influences the estimation, simulation and prediction of continuous-time models. A sampled signal is characterized only by its values at the sampling instants. However, when you apply a continuous-time input to a continuous-time system, the output values at the sampling instants depend on the inputs at the sampling instants and on the inputs between these points.

Modeling Multiple-Output Systems

Use a multiple-output modeling technique that suits the complexity and internal input-output coupling of your system.

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.

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.

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.

Next Steps After Getting an Accurate Model

How you can work with identified models.

注目の例