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Developing Models from Experimental Data using System Identification Toolbox

by Rajiv Singh

 

24 Apr 2007 (Updated 20 Apr 2010)

Describe linear and nonlinear data-centric modeling approaches.

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Description

These files support the webinar titled "Developing Models from Experimental Data Using System Identification Toolbox". The webinar was presented on April 05, 2007 and can be found at:
http://www.mathworks.com/wbnr30317

For other webinars and demos related to System Identification Toolbox, please visit: http://www.mathworks.com/products/sysid/

Contents:
1. webinar_walk_through.m: contains all the linear and nonlinear estimation examples presented during the webinar.

2. Data files and Simulink models: process_data.mat, ExampleModel.mdl, Friction_Model.mdl. Any other data files used in the presentation already ship with the toolbox (ver 7.0).

Products used:
- You basically need only System Identification Toolbox (SITB) to try out most examples.

- To use Simulink blocks, you would, of course, need Simulink.

- Control System Toolbox is used at one place to show how estimated models can be converted into LTI objects (SS, TF etc)

- Optimization Toolbox will be used if available for grey box estimation. If not, SITB's built-in optimizers will be used automatically.

- Other products mentioned: Neural Network Toolbox, Model Predictive Control Toolbox and Robust Control Toolbox.

Required Products Simulink
System Identification Toolbox
MATLAB release MATLAB 7.4 (R2007a)
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Comments and Ratings (7)
02 Sep 2007 Yang Zhang

A good webinar with excellent support file.

21 Feb 2008 sanjay kanani

we want to simulink model of system identification with their m.file

05 Jul 2009 Nataraj Hariahran  
02 Dec 2010 Farha

Explanation was lucid. Understandable. One thing needed was to use those parameters as in transfer function form.

09 Oct 2011 Mr Smart  
12 Oct 2011 Cristóbal Samudio

A good Webinar.. excelent!!!
I have a question: It is possible to generate a forecast tool with the help of the System Identification (starting from historical time series).
I think the answer is yes (but I am not sure), because I can see the Discrete-time IDPOLY model which is the mathematical model that difine the historical data (using the display command).
In the case it is possible, which are commands to use?

24 Nov 2011 Rajiv Singh

For forecasting the response of linear time series model, see:

http://www.mathworks.com/matlabcentral/answers/12186-forecasting-function

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Updates
28 Jul 2009

clean up

28 Jan 2010

Updated description. Added link to webinar. Updated walk through demo to mention some product changes after R2007a.

28 Jan 2010

updated description; added link to webinar
updated walk through demo contents
removed webinar related slides

20 Apr 2010

Added keywords

Tag Activity for this File
Tag Applied By Date/Time
identification Rajiv Singh 22 Oct 2008 09:10:15
optimization Rajiv Singh 22 Oct 2008 09:10:15
control Rajiv Singh 22 Oct 2008 09:10:15
estimation Rajiv Singh 22 Oct 2008 09:10:15
nonlinear Rajiv Singh 22 Oct 2008 09:10:15
nonlinear model Rajiv Singh 01 Feb 2010 10:23:16
prediction Rajiv Singh 01 Feb 2010 10:23:16
arx Rajiv Singh 01 Feb 2010 10:23:16
hammersteinwiener Rajiv Singh 01 Feb 2010 10:23:16
binary tree Rajiv Singh 01 Feb 2010 10:23:16
neural network Rajiv Singh 01 Feb 2010 10:23:16
wavelets Rajiv Singh 01 Feb 2010 10:23:16
control cxl chen 18 Apr 2010 23:12:54
narx Rajiv Singh 21 Apr 2010 09:36:54
hammerstein wiener Rajiv Singh 21 Apr 2010 09:36:54
narmax Rajiv Singh 21 Apr 2010 09:36:54
narx Kenneth 09 Mar 2011 02:44:03
system identification Pilot.5 Musa TOKKAN 15 Jun 2011 06:44:03
ident Pilot.5 Musa TOKKAN 15 Jun 2011 06:45:07
control sepribo 08 Mar 2012 06:22:47
nonlinear model Gordon 17 Mar 2012 15:12:54

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