Mercurial > hg > ltpda
view m-toolbox/html_help/help/ug/sigproc_polyfit.html @ 3:960fe1aa1c10 database-connection-manager
Add LTPDADatabaseConnectionManager implementation. Java code
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>Polynomial Fitting (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_fit.html"><img src="b_prev.gif" border="0" align= "bottom" alt="Fitting Algorithms"></a> <a href= "sigproc_linear_param_estimation_svd.html"><img src="b_next.gif" border="0" align= "bottom" alt="Linear Parameter Estimation with Singular Value Decomposition"></a></td> </tr> </table> <h1 class="title"><a name="f3-12899" id="f3-12899"></a>Polynomial Fitting</h1> <hr> <p> <p> <a href="matlab:doc('ao/polyfit')">polyfit.m</a> overloads the polyfit() function of MATLAB for Analysis Objects.<br/> The script calls the following MATLAB functions: <ul> <li> polyfit.m </li> <li> polyval.m </li> </ul> <h2><a name="usage">Usage</a></h2> <div class="fragment"><pre> <span class="comment">% CALL: b = polyfit(a, pl)</span> <span class="comment">%</span> <span class="comment">% Parameters: 'N' - degree of polynomial to fit</span> <span class="comment">% 'coeffs' - (optional) coefficients</span> <span class="comment">% formed e.g. by [p,s] = polyfit(x,y,N);</span> </pre></div> The MATLAB function polyfit.m finds the coefficients of the polynomial p(x) of degree N that fits the vector 'x' to the vector 'y', in a least squares sense.<br/> After this in the script <a href="matlab:doc('ao/polyfit')">polyfit.m</a> the function polyval.m is called, which evaluates the polynomial of order 'N' according to these coefficients. <br/> Using the output of polyval.m the fitted data series is created and outputted as analysis object. </p> </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_fit.html"><img src= "b_prev.gif" border="0" align="bottom" alt= "Fitting Algorithms"></a> </td> <td align="left">Fitting Algorithms</td> <td> </td> <td align="right">Linear Parameter Estimation with Singular Value Decomposition</td> <td align="right" width="20"><a href= "sigproc_linear_param_estimation_svd.html"><img src="b_next.gif" border="0" align= "bottom" alt="Linear Parameter Estimation with Singular Value Decomposition"></a></td> </tr> </table><br> <p class="copy">©LTP Team</p> </body> </html>