Process Models

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

Apps

Identify models of dynamic systems from measured data

Estimate continuous-time process model for single-input, single-output (SISO) system in either time or frequency domain in the Live Editor

`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

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

How to estimate process models at the command line.

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

This example shows how to build simple process models using System Identification Toolbox™.

Definition of a process model.

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

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

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

Specify a noise model.

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