Main Content

最新のリリースでは、このページがまだ翻訳されていません。 このページの最新版は英語でご覧になれます。

モデル タイプとその他の変換

制御デザインのためのモデル タイプの変換、モデル次数の低次元化


idfrdFrequency-response data or model
idpolyPolynomial model with identifiable parameters
idtfTransfer function model with identifiable parameters
idssState-space model with identifiable parameters
noisecnvTransform identified linear model with noise channels to model with measured channels only
translatecovTranslate parameter covariance across model transformation operations
mergeMerge estimated models
noise2measNoise component of model
absorbDelayむだ時間を z = 0 または移相シフトでの極に置き換える
chgFreqUnit周波数応答データ モデルの周波数単位の変更
fdel周波数応答データ (FRD) モデルから指定したデータを削除


Transforming Between Linear Model Representations

Converting between state-space, polynomial, and frequency-response representations.

Reducing Model Order Using Pole-Zero Plots

You can use pole-zero plots of linear identified models to evaluate whether it might be useful to reduce model order. When confidence intervals for a pole-zero pair overlap, this overlap indicates a possible pole-zero cancellation.

Create and Plot Identified Models Using Control System Toolbox Software

Identify models and use the Linear System Analyzer to plot the models.


Using Identified Models for Control Design Applications

Using System Identification Toolbox™ models with Control System Toolbox™ software.

Subreferencing Models

Creating models with subsets of inputs and outputs from multivariable models at the command line.

Canonical State-Space Realizations

Modal, companion, observable and controllable canonical state-space models.

Concatenating Models

Horizontal and vertical concatenation of model objects at the command line.

Merging Models

How to merge models to obtain a single model with parameters that are statistically weighed means of the parameters of the individual models.

Treating Noise Channels as Measured Inputs

Convert noise channels to measured channels and include the variance of the innovations.