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author Daniele Nicolodi <nicolodi@science.unitn.it>
date Wed, 23 Nov 2011 19:22:13 +0100
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1 <!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"
2 "http://www.w3.org/TR/1999/REC-html401-19991224/loose.dtd">
3
4 <html lang="en">
5 <head>
6 <meta name="generator" content=
7 "HTML Tidy for Mac OS X (vers 1st December 2004), see www.w3.org">
8 <meta http-equiv="Content-Type" content=
9 "text/html; charset=us-ascii">
10
11 <title>Polynomial Fitting (LTPDA Toolbox)</title>
12 <link rel="stylesheet" href="docstyle.css" type="text/css">
13 <meta name="generator" content="DocBook XSL Stylesheets V1.52.2">
14 <meta name="description" content=
15 "Presents an overview of the features, system requirements, and starting the toolbox.">
16 </head>
17
18 <body>
19 <a name="top_of_page" id="top_of_page"></a>
20
21 <p style="font-size:1px;">&nbsp;</p>
22
23 <table class="nav" summary="Navigation aid" border="0" width=
24 "100%" cellpadding="0" cellspacing="0">
25 <tr>
26 <td valign="baseline"><b>LTPDA Toolbox</b></td><td><a href="../helptoc.html">contents</a></td>
27
28 <td valign="baseline" align="right"><a href=
29 "sigproc_fit.html"><img src="b_prev.gif" border="0" align=
30 "bottom" alt="Fitting Algorithms"></a>&nbsp;&nbsp;&nbsp;<a href=
31 "sigproc_linear_param_estimation_svd.html"><img src="b_next.gif" border="0" align=
32 "bottom" alt="Linear Parameter Estimation with Singular Value Decomposition"></a></td>
33 </tr>
34 </table>
35
36 <h1 class="title"><a name="f3-12899" id="f3-12899"></a>Polynomial Fitting</h1>
37 <hr>
38
39 <p>
40 <p>
41 <a href="matlab:doc('ao/polyfit')">polyfit.m</a> overloads the polyfit() function of MATLAB for Analysis Objects.<br/>
42 The script calls the following MATLAB functions:
43 <ul>
44 <li> polyfit.m </li>
45 <li> polyval.m </li>
46 </ul>
47 <h2><a name="usage">Usage</a></h2>
48 <div class="fragment"><pre>
49 <span class="comment">% CALL: b = polyfit(a, pl)</span>
50 <span class="comment">%</span>
51 <span class="comment">% Parameters: 'N' - degree of polynomial to fit</span>
52 <span class="comment">% 'coeffs' - (optional) coefficients</span>
53 <span class="comment">% formed e.g. by [p,s] = polyfit(x,y,N);</span>
54 </pre></div>
55 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/>
56 Using the output of polyval.m the fitted data series is created and outputted as analysis object.
57 </p>
58
59
60 </p>
61
62 <br>
63 <br>
64 <table class="nav" summary="Navigation aid" border="0" width=
65 "100%" cellpadding="0" cellspacing="0">
66 <tr valign="top">
67 <td align="left" width="20"><a href="sigproc_fit.html"><img src=
68 "b_prev.gif" border="0" align="bottom" alt=
69 "Fitting Algorithms"></a>&nbsp;</td>
70
71 <td align="left">Fitting Algorithms</td>
72
73 <td>&nbsp;</td>
74
75 <td align="right">Linear Parameter Estimation with Singular Value Decomposition</td>
76
77 <td align="right" width="20"><a href=
78 "sigproc_linear_param_estimation_svd.html"><img src="b_next.gif" border="0" align=
79 "bottom" alt="Linear Parameter Estimation with Singular Value Decomposition"></a></td>
80 </tr>
81 </table><br>
82
83 <p class="copy">&copy;LTP Team</p>
84 </body>
85 </html>