Commands for Model Estimation :: Using This Product (System Identification Toolbox™)

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R2011b Documentation → System Identification Toolbox
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Contents

Index

• Getting Started

• Product Overview

What Is System Identification?

About Dynamic Systems and Models

System Identification Requires Measured Data

Building Models from Data

Black-Box Modeling

Grey-Box Modeling

Evaluating Model Quality

• Using This Product

When to Use the GUI Versus the Command Line

System Identification Workflow

Commands for Model Estimation

Tutorials to Help You Get Started

• Tutorial - Identifying Linear Models Using the GUI

Data Description

Loading Data into the MATLAB Workspace

Opening the System Identification Tool GUI

Importing Data Arrays into the System Identification Tool

Plotting and Processing Data

How to Estimate Linear Models Using Quick Start

Types of Quick Start Linear Models

Validating the Quick Start Models

Strategy for Estimating Accurate Models

Estimating Possible Model Orders

Identifying State-Space Models

Identifying ARMAX Input-Output Polynomial Models

Choosing the Best Model

Viewing Model Parameter Values

Viewing Parameter Uncertainties

• Tutorial - Identifying Low-Order Transfer Functions (Process Models) Using the GUI

Data Description

Loading Data into the MATLAB Workspace

Opening the System Identification Tool GUI

Importing Data Objects into the System Identification Tool

Plotting and Processing Data

Estimating a Second-Order Transfer Function Using Default Settings

Tips for Specifying Known Parameters

Validating the Model

Estimating a Second-Order Transfer Function with Complex Poles and Noise

Validating the Models

Viewing Model Parameter Values

Viewing Parameter Uncertainties

Prerequisites for This Tutorial

Preparing Input Data

Building the Simulink Model

Configuring Blocks and Simulation Parameters

Running the Simulation

• Tutorial - Identifying Linear Models Using the Command Line

Data Description

Loading Data into the MATLAB Workspace

Plotting the Input/Output Data

Removing Equilibrium Values from the Data

Using Objects to Represent Data for System Identification

Creating iddata Objects

Plotting the Data in a Data Object

Selecting a Subset of the Data

Why Estimate Step- and Frequency-Response Models?

Estimating the Frequency Response

Estimating the Step Response

Why Estimate Delays?

Estimating Delays Using the ARX Model Structure

Estimating Delays Using Alternative Methods

Why Estimate Model Order?

Commands for Estimating the Model Order

Model Order for the First Input-Output Combination

Model Order for the Second Input-Output Combination

Specifying the Structure of the Process Model

Viewing the Model Structure and Parameter Values

Specifying Initial Guesses for Time Delays

Estimating Model Parameters Using pem

Validating the Process Model

Estimating a Transfer Function with a Noise Model

Model Orders for Estimating Polynomial Models

Estimating a Linear ARX Model

Estimating a State-Space Model

Estimating a Box-Jenkins Model

Comparing Model Output to Measured Output

Simulating the Model Output

Predicting the Future Output

• Tutorial - Identifying Nonlinear Black-Box Models Using the GUI

Data Description

Types of Nonlinear Black-Box Models

What Is a Nonlinear ARX Model?

What Is a Hammerstein-Wiener Model?

Loading Data into the MATLAB Workspace

Creating iddata Objects

Starting the System Identification Tool

Importing Data Objects into the System Identification Tool

Estimating a Nonlinear ARX Model with Default Settings

Plotting Nonlinearity Cross-Sections for Nonlinear ARX Models

Changing the Nonlinear ARX Model Structure

Selecting a Subset of Regressors in the Nonlinear Block

Specifying a Previously-Estimated Model with Different Nonlinearity

Selecting the Best Model

Estimating Hammerstein-Wiener Models with Default Settings

Plotting the Nonlinearities and Linear Transfer Function

Changing the Hammerstein-Wiener Model Input Delay

Changing the Nonlinearity Estimator in a Hammerstein-Wiener Model

Selecting the Best Model

• User's Guide

• Release Notes

Commands for Model Estimation

The following table summarizes System Identification Toolbox estimation commands. For detailed information about using each command, see the corresponding reference page.

Commands for Constructing and Estimating Models

Model Type	Estimation Commands
Continuous-time low-order transfer functions (process models)	`pem`
Linear input-output polynomial models	`armax` (ARMAX only) `arx` (ARX only) `bj` (BJ only) `iv4` (ARX only) `oe` (OE only) `pem` (for all models)
State-space models	`n4sid` `pem`
Linear time-series models	`ar` `arx` (for multiple outputs) `ivar`
Nonlinear ARX models	`nlarx`
Hammerstein-Wiener models	`nlhw`

System Identification Workflow

System Identification Workflow

Tutorials to Help You Get Started

Tutorials to Help You Get Started

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