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author | Daniele Nicolodi <nicolodi@science.unitn.it> |
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date | Mon, 05 Dec 2011 16:20:06 +0100 |
parents | f0afece42f48 |
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<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" "http://www.w3.org/TR/1999/REC-html401-19991224/loose.dtd"> <html lang="en"> <head> <meta name="generator" content= "HTML Tidy for Mac OS X (vers 1st December 2004), see www.w3.org"> <meta http-equiv="Content-Type" content= "text/html; charset=us-ascii"> <title>Linear Parameter Estimation with Singular Value Decomposition (LTPDA Toolbox)</title> <link rel="stylesheet" href="docstyle.css" type="text/css"> <meta name="generator" content="DocBook XSL Stylesheets V1.52.2"> <meta name="description" content= "Presents an overview of the features, system requirements, and starting the toolbox."> </head> <body> <a name="top_of_page" id="top_of_page"></a> <p style="font-size:1px;"> </p> <table class="nav" summary="Navigation aid" border="0" width= "100%" cellpadding="0" cellspacing="0"> <tr> <td valign="baseline"><b>LTPDA Toolbox</b></td><td><a href="../helptoc.html">contents</a></td> <td valign="baseline" align="right"><a href= "sigproc_polyfit.html"><img src="b_prev.gif" border="0" align= "bottom" alt="Polynomial Fitting"></a> <a href= "sigproc_example_ao_linlsqsvd.html"><img src="b_next.gif" border="0" align= "bottom" alt="Linear least squares with singular value deconposition - single experiment"></a></td> </tr> </table> <h1 class="title"><a name="f3-12899" id="f3-12899"></a>Linear Parameter Estimation with Singular Value Decomposition</h1> <hr> <p> <!-- ================================================== --> <!-- BEGIN CONTENT FILE --> <!-- ================================================== --> <!-- ===== link box: Begin ===== --> <p> <table border="1" width="80%"> <tr> <td> <table border="0" cellpadding="5" class="categorylist" width="100%"> <colgroup> <col width="37%"/> <col width="63%"/> </colgroup> <tbody> <tr valign="top"> <td> <a href="#linlsqsvd_1exp">ao/linlsqsvd</a> </td> <td>Linear least squares with singular value deconposition - single experiment.</td> </tr> <tr valign="top"> <td> <a href="#linlsqsvd_Nexp">matrix/linlsqsvd</a> </td> <td>Linear least squares with singular value deconposition - multiple experiments.</td> </tr> <tr valign="top"> <td> <a href="#linfitsvd1">matrix/linfitsvd</a> </td> <td>Iterative linear parameter estimation for multichannel systems - symbolic system model in frequency domain.</td> </tr> <tr valign="top"> <td> <a href="#linfitsvd2">matrix/linfitsvd</a> </td> <td>Iterative linear parameter estimation for multichannel systems - ssm system model in time domain.</td> </tr> <tr valign="top"> <td> <a href="#ref">References</a> </td> </tr> </tbody> </table> </td> </tr> </table> </p> <!-- ===== link box: End ====== --> <p> </p> <p> The following sections gives an introduction to the linear parameters estimation methods based on singular value decomposition. </p> <!-- ===== ao/linlsqsvd ====== --> <h2><a name="#linlsqsvd_1exp">Linear least squares with singular value deconposition - single experiment.</a></h2> <p> We report an <a href="sigproc_example_ao_linlsqsvd.html">example</a> of the application of <a href="matlab:doc('ao/linlsqsvd')">ao/linlsqsvd</a>. The <a href="sigproc_example_ao_linlsqsvd.html">example</a> shows how to perform a linear parameters estimation for a single data series which is representing the output of an experiment on a given physical system. </p> <!-- ===== matrix/linlsqsvd ====== --> <h2><a name="#linlsqsvd_Nexp">Linear least squares with singular value deconposition - multiple experiments.</a></h2> <p> We report an <a href="sigproc_example_matrix_linlsqsvd.html">example</a> of the application of <a href="matlab:doc('matrix/linlsqsvd')">matrix/linlsqsvd</a>. The <a href="sigproc_example_matrix_linlsqsvd.html">example</a> shows how to perform a linear parameters estimation for multiple data series which are representing the output of multiple experiments on a given physical system. </p> <!-- ===== matrix/linfitsvd ====== --> <h2><a name="#linfitsvd1">Iterative linear parameter estimation for multichannel systems - symbolic system model in frequency domain.</a></h2> <p> We report an <a href="sigproc_example_matrix_linfitsvd.html">example</a> of the application of <a href="matlab:doc('matrix/linfitsvd')">matrix/linfitsvd</a>. The <a href="sigproc_example_matrix_linfitsvd.html">example</a> shows how to perform an iterative linear parameters estimation for a multichannel system. System model is analystic and frequency domain. Fit is performed in time domain. Further details can be found in ref. [1]. </p> <!-- ===== matrix/linfitsvd ====== --> <h2><a name="#linfitsvd2">Iterative linear parameter estimation for multichannel systems - ssm system model in time domain.</a></h2> <p> We report an <a href="sigproc_example_matrix_linfitsvd_ssm.html">example</a> of the application of <a href="matlab:doc('matrix/linfitsvd')">matrix/linfitsvd</a>. The <a href="sigproc_example_matrix_linfitsvd_ssm.html">example</a> shows how to perform an iterative linear parameters estimation for a multichannel system. System model is ssm and time domain. Fit is performed in time domain. </p> <h2><a name="#ref"> References</a></h2> <ol> <li> M Nofrarias, L Ferraioli, G Congedo, Comparison of parameter estimates results in STOC Exercise 6, S2-AEI-TN-3070.</li> </ol> </p> <br> <br> <table class="nav" summary="Navigation aid" border="0" width= "100%" cellpadding="0" cellspacing="0"> <tr valign="top"> <td align="left" width="20"><a href="sigproc_polyfit.html"><img src= "b_prev.gif" border="0" align="bottom" alt= "Polynomial Fitting"></a> </td> <td align="left">Polynomial Fitting</td> <td> </td> <td align="right">Linear least squares with singular value deconposition - single experiment</td> <td align="right" width="20"><a href= "sigproc_example_ao_linlsqsvd.html"><img src="b_next.gif" border="0" align= "bottom" alt="Linear least squares with singular value deconposition - single experiment"></a></td> </tr> </table><br> <p class="copy">©LTP Team</p> </body> </html>