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R2012a Documentation → System Identification Toolbox
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Contents

Index

• Getting Started

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

Data Description

Loading Data into the MATLAB Workspace

Opening the System Identification Tool

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 Transfer Function Models

Identifying State-Space Models

Identifying ARMAX Input-Output Polynomial Models

Choosing the Best Model

Viewing Model Parameter Values

Viewing Parameter Uncertainties

Data Description

Loading Data into the MATLAB Workspace

Opening the System Identification Tool

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 Process Model with Complex Poles

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

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 Empirical 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 Transfer Function

Validating the Process Model

Residual Analysis

Specifying the Structure of the Process Model

Viewing the Model Structure and Parameter Values

Specifying Initial Guesses for Time Delays

Estimating Model Parameters Using procest

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

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

• Data Import and Processing

• Linear Model Identification

• Nonlinear Black-Box Model Identification

• ODE Parameter Estimation

• Recursive Model Identification

• Model Analysis

forecastOptions

• Simulation and Prediction

• System Identification Tool GUI

• Release Notes

present - Display model information, including estimated uncertainty

Syntax

present(m)

Description

The present function displays the model m, together with the estimated standard deviations of the parameters, loss function, and Akaike's Final Prediction Error (FPE) Criterion (which essentially equals the AIC). It also displays information about how m was created.

m is linear or nonlinear identified model.

present thus gives more detailed information about the model than the standard display function.

See Also

getcov | getpvec | idssdata | polydata | tfdata | zpkdata

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