System Identification Toolbox

Contact Details

Prof. Brett Ninness

Email

Phone

(02) 4921 6032
+61 2 4921 6032 (intl)

Fax

(02) 4921 6993

Office

Callaghan Campus
Building EA: EA-G29

Post

Prof. Brett Ninness

School of Electrical Engineering and Computer Science

- - -

Funding

Australian Research Council

ARC Discovery Project
DP0666955
2006-2008
Value: $336,000

Australian Research Council

ARC Discovery Project
DP0208665
2002-2005
Value: $360,000
This toolbox is a MATLAB-based software package for the estimation of dynamic systems. A wide range of standard estimation approaches are supported. These include the use of non-parametric, subspace-based and prediction-error algorithms coupled (in the latter case) with either MIMO state space or MISO polynomial model structures.
Contents

Background

Details of the project.

Screenshots

Example toolbox output graphics.

Screencasts

Short Tutorials that demonstrate usage of the GUI.

Download

Free for non-commercial use. Registration required.

Publications

Publications related to this project.

Project Team

Background to the people working on this project.

Features

A key feature of this software is the implementation of several new techniques which include the use of non-standard model parametrizations, the employment of Expectation Maximisation (EM) methods and methods for the estimation of non-linear models,.

  • Latest version: May 2007;
  • Free for non-commercial use;
  • MATLAB based;

Key new features in release 2.0 include:

  • Support for arbitrary sampling points in continuous-time state-space model estimation (MIMO);
  • Structured state-space models (MIMO, discrete-time data);
  • State-space models for frequency domain data (MIMO);
  • Subspace, Expectation-Maximisation and Gradient-based search algorithms added for estimating MIMO state-space models from frequency data;
  • Trust-region and Quasi-Newton methods supported in optimisation routine;
  • Many bugfixes and minor enhancements to existing capabilities. For example, est(Z) automatically estimates a parametric model based only on data, and est(Z,d) estimates a model of order d with all other aspects of model specification determined automatically.

Download

This toolbox runs with Matlab under Linux, Mac OS X, and MS Windows and has been tested using Matlab versions R2006a - R2007a. It is free for non-commercial use.

→ Available here: Download

News

  • 2007-05-06:Version 2.0 of toolbox released.
  • 2005-08-25:Version 1.0 of toolbox released.
  • 2005-08-11:Website launched.



Maintained by Prof. Brett Ninness
University of Newcastle
22 Apr 2009, © Copyright