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プロセス モデル

静的ゲイン、時定数、入力/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

関数

procestEstimate process model using time or frequency data
idprocContinuous-time process model with identifiable parameters
pemPrediction error estimate for linear and nonlinear model
idparCreate parameter for initial states and input level estimation
delayestEstimate time delay (dead time) from data
initSet or randomize initial parameter values
getpvecModel parameters and associated uncertainty data
setpvecModify value 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

Import data into the app, and specify model parameters and estimation options.

Estimate Process Models at the Command Line

How to estimate process models at the command line.

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

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

Building and Estimating Process Models Using System Identification Toolbox™

This example shows how to build simple process models using System Identification Toolbox™. Techniques for creating these models and estimating their parameters using experimental data is described. This example requires Simulink®.

概念

What Is a Process Model?

Definition of a process model.

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.