Contents

Index

• Getting Started

• Product Overview

Why Use This Toolbox?

Related Products

• About System Identification

System Identification Process

Types of Data You Can Model

• About Dynamic Models

What Is a Dynamic Model?

What Is a Model Structure?

Linear Versus Nonlinear Models

Models From First Principles

Black-Box Models

• Using This Product

When to Use the GUI Versus the Command Line

Starting This Toolbox

Steps for Using This Toolbox

Commands for Model Estimation

Tutorials to Help You Get Started

• Tutorial - Identifying Linear Models Using the GUI

• About This Tutorial

Data Description

• Preparing Data for System Identification

Loading Data into the MATLAB Workspace

Opening the System Identification Tool GUI

Importing Data Arrays into the System Identification Tool

Plotting and Processing Data

Saving the GUI Session

• Estimating Linear Models Using Quick Start

How to Estimate Linear Models Using Quick Start

Types of Quick Start Linear Models

Validating the Quick Start Models

• Estimating Accurate Linear 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 Parameters

Viewing Model Parameter Values

Viewing Parameter Uncertainties

Exporting the Model to the MATLAB Workspace

Exporting the Model to the LTI Viewer

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

• About This Tutorial

Data Description

What Is a Continuous-Time Process Model?

• Preparing Data for System Identification

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 (Process Model) with Complex Poles

Estimating a Second-Order Transfer Function Using Default Settings

Tips for Specifying Known Parameters

Validating the Model

• Estimating a Transfer Function with a Noise Model

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

Validating the Models

• Viewing Model Parameters

Viewing Model Parameter Values

Viewing Parameter Uncertainties

Exporting the Model to the MATLAB Workspace

• Simulating a System Identification Toolbox Model in Simulink Software

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

• About This Tutorial

Data Description

• Preparing Data

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

• Estimating Step- and Frequency-Response Models

Why Estimate Step- and Frequency-Response Models?

Estimating the Frequency Response

Estimating the Step Response

• Estimating Delays in the Multiple-Input System

Why Estimate Delays?

Estimating Delays Using the ARX Model Structure

Estimating Delays Using Alternative Methods

• Estimating Model Orders Using an ARX Model Structure

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

• Estimating Continuous-Time Transfer Functions (Process Models)

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

• Estimating Black-Box Polynomial Models

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 and Predicting Model Output

Simulating the Model Output

Predicting the Future Output

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

• About This Tutorial

Data Description

• What Are Nonlinear Black-Box Models?

Types of Nonlinear Black-Box Models

What Is a Nonlinear ARX Model?

What Is a Hammerstein-Wiener Model?

• Preparing Data

Loading Data into the MATLAB Workspace

Creating iddata Objects

Starting the System Identification Tool

Importing Data Objects into the System Identification Tool

• Estimating Nonlinear ARX Models

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

Efficiently Modifying Model Structure for Estimating Nonlinear ARX Models

Selecting the Best Model

• Estimating Hammerstein-Wiener Models

Estimating Hammerstein-Wiener Models with Default Settings

Plotting the Nonlinearities and Linear Transfer Function

Changing the Hammerstein-Wiener Model Structure

Changing the Nonlinearity Estimator in a Hammerstein-Wiener Model

Selecting the Best Model

• Release Notes

Learn more about System Identification Toolbox

Getting Started

Product Overview	Overview of how the System Identification Toolbox product supports modeling dynamic systems
About System Identification	Criteria for deciding which models to estimate for your system
Using This Product	Starting the System Identification Toolbox product and steps for performing typical modeling tasks
Tutorial – Identifying Linear Models Using the GUI	Identifying linear black-box models from single-input/single-output (SISO) data using the System Identification Tool GUI
Tutorial – Identifying Low-Order Transfer Functions (Process Models) Using the GUI	Identifying continuous-time transfer functions (process models) from single-input/single-output (SISO) data using the System Identification Tool GUI
Tutorial – Identifying Linear Models Using the Command Line	Identifying linear models from multiple-input/single-output (MISO) data using System Identification Toolbox commands
Tutorial – Identifying Nonlinear Black-Box Models Using the GUI	Identifying nonlinear black-box models from single-input/single-output (SISO) data using the System Identification Tool GUI

Product Overview

Product Overview

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