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Process Models

Low-order transfer function models with static gain, time constant, and input/output delay


System Identification Identify models of dynamic systems from measured data


procest Estimate process model using time or frequency data
idproc Continuous-time process model with identifiable parameters
pem Prediction error estimate for linear and nonlinear model
idpar Create parameter for initial states and input level estimation
delayest Estimate time delay (dead time) from data
init Set or randomize initial parameter values
getpvec Model parameters and associated uncertainty data
setpvec Modify value of model parameters
getpar Obtain attributes such as values and bounds of linear model parameters
setpar Set attributes such as values and bounds of linear model parameters
procestOptions Options set for procest

Examples and How To

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™.


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.

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