相関モデル

相関解析を使用して得られるインパルス応答モデル

アプリ

測定データからの動的システムのモデルの同定

`cra`	前置白色化ベースの相関解析を使用してインパルス応答を推定します。
`impulseest`	Nonparametric impulse response estimation
`era`	Estimate state-space model from impulse response data using Eigensystem Realization Algorithm (ERA)

`getpvec`	Obtain model parameters and associated uncertainty data
`setpvec`	Modify values of model parameters

impulseestOptions Options set for impulseest

Estimate Impulse-Response Models Using System Identification App
Estimate in the app using time-domain correlation analysis.
Estimate Impulse-Response Models at the Command Line
Use impulseest command to estimate using correlation analysis.
Compute Response Values
Obtain numerical impulse- and step-response vectors as a function of time.
Identify Delay Using Transient-Response Plots
You can use transient-response plots to estimate the input delay, or dead time, of linear systems. Input delay represents the time it takes for the output to respond to the input.

What Is Time-Domain Correlation Analysis?
Time-domain correlation analysis refers to non-parametric estimation of the impulse response of dynamic systems as a finite impulse response (FIR) model from the data. The estimated model is stored as transfer function model object (idtf). For information about transfer function models, see 伝達関数モデルとは.
Data Supported by Correlation Analysis
Characteristics of data supported for estimation of impulse-response models.
Correlation Analysis Algorithm
Correlation analysis refers to methods that estimate the impulse response of a linear model, without specific assumptions about model orders.