Learn more about System Identification Toolbox

Version 7.0 (R2007a) System Identification Toolbox Software

This table summarizes what's new in Version 7.0 (R2007a):

New Features and Changes	Version Compatibility Considerations	Fixed Bugs and Known Problems	Related Documentation at Web Site
Yes Details below	No	Bug Reports	No

New features and changes introduced in this version are:

• New Nonlinear Black-Box Modeling Options

• New Nonlinear Grey-Box Modeling Option

• New Getting Started Guide

• Revised and Expanded User's Guide

New Nonlinear Black-Box Modeling Options

You can now estimate nonlinear discrete-time black-box models for both single-output and multiple-output time-domain data. The System Identification Toolbox product supports the following types of nonlinear black-box models:

• Hammerstein-Wiener

• Nonlinear ARX

To learn how to estimate nonlinear black-box models using the System Identification Tool GUI or commands in the MATLAB Command Window, see the System Identification Toolbox documentation.

Note   You can estimate Hammerstein-Wiener black-box models from input-output data only. These models do not support time-series data, where there is no input.

New demos are available to help you explore nonlinear black-box functions. For more information, see the collection of demos in the Tutorials on Nonlinear ARX and Hammerstein-Wiener Model Identification category.

New Nonlinear Grey-Box Modeling Option

You can now estimate nonlinear discrete-time and continuous-time models for arbitrary nonlinear ordinary differential equations using single-output and multiple-output time-domain data, or time-series data (no measured inputs). Models that you can specify as a set of nonlinear ordinary differential equations (ODEs) are called grey-box models.

To learn how to estimate nonlinear grey-box models using the commands in the MATLAB Command Window, see System Identification Toolbox documentation.

Specify the ODE in an M-file or a MEX-file. The template file for writing the MEX-file, IDNLGREY_MODEL_TEMPLATE.c, is located in matlab/toolbox/ident/nlident.

To estimate the equation parameters, first construct an idnlgrey object to specify the ODE file and the parameters you want to estimate. Use pem to estimate the ODE parameters. For more information, see the idnlgrey and pem reference pages.

New demos are available to help you explore nonlinear grey-box functions. For more information, see the collection of demos in the Tutorials on Nonlinear Grey-Box Model Identification category.

Optimization Toolbox Search Method for Nonlinear Estimation Is Supported

If you have Optimization Toolbox software installed, you can specify the lsqnonlin search method for estimating black-box and grey-box nonlinear models in the MATLAB Command Window.

model.algorithm.searchmethod='lsqnonlin'

For more information, see the idnlarx, idnlhw, and idnlgrey reference pages.

New Getting Started Guide

The System Identification Toolbox product now provides a new Getting Started Guide. This guide introduces fundamental identification concepts and provides the following tutorials to help you get started quickly:

• Tutorial – Identifying Linear Models Using the GUI — Tutorial for using the System Identification Tool graphical user interface (GUI) to estimate linear black-box models for single-input and single-output (SISO) data.

• Tutorial – Identifying Low-Order Transfer Functions (Process Models) Using the GUI — Tutorial for using the System Identification Tool graphical user interface (GUI) to estimate low-order transfer functions to fit single-input and single-output (SISO) data.

• Tutorial – Identifying Linear Models Using the Command Line — Tutorial for estimating models using System Identification Toolbox objects and methods for multiple-input and single-output (MISO) data.

Revised and Expanded User's Guide

The System Identification Toolbox documentation has been revised and expanded.

Version 7.1 (R2007b) System Identification Toolbox Software

Version 6.2 (R2006b) System Identification Toolbox Software

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