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プロセス モデル

静的ゲイン、時定数、入出力遅延をもつ低次伝達関数モデル

アプリ

System IdentificationIdentify models of dynamic systems from measured data

ライブ エディター タスク

プロセス モデルの推定Estimate continuous-time process model for single-input, single-output (SISO) system in either time or frequency domain in the Live Editor

関数

procestEstimate process model using time or frequency data
idprocContinuous-time process model with identifiable parameters
pemPrediction error minimization for refining linear and nonlinear models
idparCreate parameter for initial states and input level estimation
delayestEstimate time delay (dead time) from data
initSet or randomize initial parameter values
getpvecObtain model parameters and associated uncertainty data
setpvecModify values of model parameters
getparObtain attributes such as values and bounds of linear model parameters
setparSet attributes such as values and bounds of linear model parameters
procestOptionsOptions set for procest

例および操作のヒント

Estimate Process Models Using the App

Specify model parameters and estimation options to use for estimating a process model.

Estimate Process Models at the Command Line

Estimate first-order process models with fully free parameters and with a combination of fixed and free parameters.

Identify Low-Order Transfer Functions (Process Models) Using System Identification App

Identify continuous-time transfer functions from single-input/single-output (SISO) data using the app.

System Identification Toolbox™ を使用したプロセス モデルの構築と推定

この例では、System Identification Toolbox™ を使用して、シンプルなプロセス モデルを構築する方法を説明します。

概念

What Is a Process Model?

A process model is a simple continuous-time transfer function that describes linear system dynamics in terms of static gain, time constants, and input-output delay.

Process Model Structure Specification

Configure the model structure by specifying the number of real or complex poles, and whether to include a zero, delay, and integrator.

Data Supported by Process Models

Use regularly sampled time-domain and frequency-domain data, and continuous-time frequency-domain data.

Estimating Multiple-Input, Multi-Output Process Models

Specify whether to estimate the same transfer function for all input-output pairs, or a different transfer function for each pair.

Disturbance Model Structure for Process Models

Specify a noise model.

Specifying Initial Conditions for Iterative Estimation Algorithms

Specify how the algorithm treats initial conditions for estimation of model parameters.