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author Daniele Nicolodi <nicolodi@science.unitn.it>
date Mon, 05 Dec 2011 16:20:06 +0100
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1 <!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"
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2 "http://www.w3.org/TR/1999/REC-html401-19991224/loose.dtd">
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3
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4 <html lang="en">
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5 <head>
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6 <meta name="generator" content=
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7 "HTML Tidy for Mac OS X (vers 1st December 2004), see www.w3.org">
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8 <meta http-equiv="Content-Type" content=
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9 "text/html; charset=us-ascii">
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10
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11 <title>System identification in z-domain (LTPDA Toolbox)</title>
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12 <link rel="stylesheet" href="docstyle.css" type="text/css">
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13 <meta name="generator" content="DocBook XSL Stylesheets V1.52.2">
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14 <meta name="description" content=
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15 "Presents an overview of the features, system requirements, and starting the toolbox.">
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16 </head>
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17
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18 <body>
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19 <a name="top_of_page" id="top_of_page"></a>
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20
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21 <p style="font-size:1px;">&nbsp;</p>
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22
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23 <table class="nav" summary="Navigation aid" border="0" width=
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24 "100%" cellpadding="0" cellspacing="0">
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25 <tr>
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26 <td valign="baseline"><b>LTPDA Toolbox</b></td><td><a href="../helptoc.html">contents</a></td>
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27
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28 <td valign="baseline" align="right"><a href=
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29 "ltpda_training_topic_5.html"><img src="b_prev.gif" border="0" align=
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30 "bottom" alt="Topic 5 - Model fitting"></a>&nbsp;&nbsp;&nbsp;<a href=
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31 "ltpda_training_topic_5_2.html"><img src="b_next.gif" border="0" align=
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32 "bottom" alt="Generation of noise with given PSD"></a></td>
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33 </tr>
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34 </table>
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35
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36 <h1 class="title"><a name="f3-12899" id="f3-12899"></a>System identification in z-domain</h1>
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37 <hr>
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38
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39 <p>
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40 <p>
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41 System identification in Z-domain is performed with the function <tt>ao/zDomainFit</tt>.
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42 It is based on a modified version of the vector fitting algorithm that was
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43 adapted to fit in the Z-domain. Details of the agorithm can be found in the
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44 <a href="zdomainfit.html">Z-domain fit documentation page</a></li>.
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45 </p>
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46
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47 <h2> System identification in Z-domain </h2>
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48
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49 <p>
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50 During this exercise we will:
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51 <ol>
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52 <li> Generate white noise
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53 <li> Filter white noise with a <tt>miir</tt> filter generated by a <tt> pzmodel</tt>
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54 <li> Extract the transfer function from data
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55 <li> Fit the transfer function with <tt>ao/zDomainFit</tt>
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56 <li> Check results
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57 </ol>
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58 </p>
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59
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60 <p>
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61 Let's start by generating some white noise.
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62 </p>
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63
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64 <div class="fragment"><pre>
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65 a = ao(plist(<span class="string">'tsfcn'</span>, <span class="string">'randn(size(t))'</span>, <span class="string">'fs'</span>, 1, <span class="string">'nsecs'</span>, 10000,<span class="string">'yunits'</span>,<span class="string">'m'</span>));
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66 </pre></div>
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67 <p>
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68 This command generates a time series of gaussian distributed random noise
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69 with a sampling frequency (<span class="string">'fs'</span>) of 1 Hz,
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70 10000 seconds long (<span class="string">'nsecs'</span>) and with
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71 <span class="string">'yunits'</span> set to meters (<span class="string">'m'</span>).
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72 </p>
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73 <p>
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74 Now we are ready to move on the second step where we will:
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75 <ul>
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76 <li> Build a pole-zero model (<tt>pzmodel</tt>)
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77 <li> Construct a <tt>miir</tt> filter from the <tt>pzmodel</tt>
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78 <li> filter white noise data with the <tt>miir</tt> filter in order to obtain a
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79 colored noise time series.
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80 </ul>
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81
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82 </p>
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83 <div class="fragment"><pre>
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84 pzm = pzmodel(1, [0.005 2], [0.05 4]);
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85
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86 filt = miir(pzm, plist(<span class="string">'fs'</span>, 1, <span class="string">'name'</span>, <span class="string">'None'</span>));
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87 filt.setIunits(<span class="string">'m'</span>);
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88 filt.setOunits(<span class="string">'V'</span>);
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89
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90 <span class="comment">% Filter the data</span>
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91 ac = filter(a,filt);
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92 ac.simplifyYunits;
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93 </pre></div>
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94 <p>
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95 We can calculate the PSD of the data streams in order to check the difference between the coloured noise and the
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96 white noise. Let's choose the log-scale estimation method.
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97 </p>
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98 <div class="fragment"><pre>
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99 axx = lpsd(a);
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100 acxx = lpsd(ac);
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101 iplot(axx,acxx)
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102 </pre></div>
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103 <p>
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104 You should obtain a plot similar to this:
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105 <div align="center">
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106 <IMG src="images/ltpda_training_1/topic5/ltpda_training_5_1_1.png" align="center" border="0">
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107 </div>
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108 </p>
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109 <p>
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110 Let us move to the third step. We will generate the transfer function from the data
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111 and split it in order to remove the first 3 bins. The last operation is useful
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112 for the fitting process.
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113 </p>
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114 <div class="fragment"><pre>
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115 tf = ltfe(a,ac);
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116 tfsp = split(tf,plist(<span class="string">'frequencies'</span>, [5e-4 5e-1]));
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117
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118 iplot(tf,tfsp)
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119 </pre></div>
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120 <p>
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121 The plot should look like the following:
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122 <div align="center">
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123 <IMG src="images/ltpda_training_1/topic5/ltpda_training_5_1_2.png" align="center" border="0">
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124 </div>
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125 </p>
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126 <p>
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127 It is now the moment to start fitting with <tt>zDomainFit</tt>. As reported in the
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128 function help page we can run an automatic search loop to identify proper model order.
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129 In such a case we have to define a set of conditions to check fit accuracy and to exit the fitting loop.<br/>
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130 We can start checking Mean Squared Error and variation (CONDTYPE = 'MSE').
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131 It checks if the normalized mean squared error is lower than the value specified in the parameter FITTOL
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132 and if the relative variation of the mean squared error is lower than the value specified in the parameter MSEVARTOL.
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133 <p><!-- Table of the set: Default -->
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134 <a name="1"/>
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135 <table cellspacing="0" class="body" cellpadding="4" summary="" width="100%" border="2">
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136 <colgroup>
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137 <col width="15%"/>
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138 <col width="20%"/>
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139 <col width="20%"/>
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140 <col width="45%"/>
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141 </colgroup>
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142 <thead>
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143 <tr valign="top">
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144 <th bgcolor="#B9C6DD" colspan="4"><h3>Default</h3></th>
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145 </tr>
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146 <tr valign="top">
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147 <th bgcolor="#D7D7D7">Key</th>
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148 <th bgcolor="#D7D7D7">Default Value</th>
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149 <th bgcolor="#D7D7D7">Options</th>
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150 <th bgcolor="#D7D7D7">Description</th>
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151 </tr>
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152 </thead>
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153 <tbody>
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154 <tr valign="top">
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155 <td bgcolor="#F2F2F2">CONDTYPE</td>
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156 <td bgcolor="#F2F2F2">'MSE'</td>
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157 <td bgcolor="#F2F2F2">
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158 <ul>
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159 <li><font color="#1111FF">'MSE'</font></li>
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160 <li><font color="#1111FF">'RLD'</font></li>
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161 <li><font color="#1111FF">'RSF'</font></li>
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162 </ul>
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163 </td>
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164 <td bgcolor="#F2F2F2">Fit conditioning type. Admitted values are:
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165 <ul>
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166 <li>'MSE' Mean Squared Error and variation</li>
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167 <li>'RLD' Log residuals difference and mean squared error variation</li>
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168 <li>'RSF' Residuals spectral flatness and mean squared error variation</li>
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169 </ul>
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170 </td>
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171 </tr>
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172 <tr valign="top">
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173 <td bgcolor="#F2F2F2">FITTOL</td>
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174 <td bgcolor="#F2F2F2">0.001</td>
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175 <td bgcolor="#F2F2F2"><i>none</i></td>
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176 <td bgcolor="#F2F2F2">Fit tolerance.</td>
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177 </tr>
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178 <tr valign="top">
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179 <td bgcolor="#F2F2F2">MSEVARTOL</td>
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180 <td bgcolor="#F2F2F2">0.01</td>
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181 <td bgcolor="#F2F2F2"><i>none</i></td>
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182 <td bgcolor="#F2F2F2">Mean Squared Error Variation - Check if the<br/>relative variation of the mean squared error is<br/>smaller than the value specified. This<br/>option is useful for finding the minimum of the Chi-squared.</td>
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183 </tr>
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184 </tbody>
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185 </table>
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186 </p>
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187 <p>You will find a list of all Parameters of zDomainFit here: <a href="matlab:web(ao.getInfo('zDomainFit').tohtml, '-helpbrowser')">Parameter of zDomainFit</a></p>
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188
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189 Now let's run the fit:
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190 </p>
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191 <div class="fragment"><pre>
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192 <span class="comment">% Set up the parameters</span>
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193 plfit = plist(<span class="string">'fs'</span>,1,... <span class="comment">% Sampling frequency for the model filters</span>
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194 <span class="string">'AutoSearch'</span>,<span class="string">'on'</span>,... <span class="comment">% Automatically search for a good model</span>
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195 <span class="string">'StartPolesOpt'</span>,<span class="string">'clog'</span>,... <span class="comment">% Define the properties of the starting poles - complex distributed in the unitary circle</span>
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196 <span class="string">'maxiter'</span>,50,... <span class="comment">% Maximum number of iteration per model order</span>
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197 <span class="string">'minorder'</span>,2,... <span class="comment">% Minimum model order</span>
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198 <span class="string">'maxorder'</span>,9,... <span class="comment">% Maximum model order</span>
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199 <span class="string">'weightparam'</span>,<span class="string">'abs'</span>,... <span class="comment">% Assign weights as 1./abs(data)</span>
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200 <span class="string">'condtype'</span>,<span class="string">'MSE'</span>,... <span class="comment">% Mean Squared Error and variation</span>
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201 <span class="string">'fittol'</span>,1e-2,... <span class="comment">% Fit tolerance</span>
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202 <span class="string">'msevartol'</span>,1e-1,... <span class="comment">% Mean Squared Error Variation tolerance</span>
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203 <span class="string">'Plot'</span>,<span class="string">'on'</span>,... <span class="comment">% Set the plot on or off</span>
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204 <span class="string">'ForceStability'</span>,<span class="string">'on'</span>,... <span class="comment">% Force to output a stable poles model</span>
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205 <span class="string">'CheckProgress'</span>,<span class="string">'off'</span>); <span class="comment">% Display fitting progress on the command window</span>
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206
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207 <span class="comment">% Do the fit</span>
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208 fobj = zDomainFit(tfsp,plfit);
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209
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210 <span class="comment">% Set the input and output units for fitted model</span>
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211 fobj.setIunits(<span class="string">'m'</span>);
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212 fobj.setOunits(<span class="string">'V'</span>);
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213 </pre></div>
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214 <p>
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215 When <span class="string">'Plot'</span> parameter is set to <span class="string">'on'</span>
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216 the function plots the fit progress.
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217 <div align="center">
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218 <IMG src="images/ltpda_training_1/topic5/ltpda_training_5_1_3.png" align="center" border="0">
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219 </div>
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220 </p>
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221
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222 <p>
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223 We can now check the result of our fitting procedures.
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224 We calculate the frequency response of the fitted models (filters) and compare them
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225 with the starting IIR filter response, then we will plot the percentage error on the filters magnitudes.
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226 </p>
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227 <p>
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228 Note that the result of the fitting procedure is a <tt>matrix</tt> object containing a <tt>filterbank</tt> object,
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229 which itself contains a parallel bank of 3 IIR filters.
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230 </p>
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231 <p>
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232 Note that at the moment we need to access the individual filter objects inside the <tt>matrix</tt> object that was the result of the fitting procedure.</p>
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233 <div class="fragment"><pre>
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234 <span class="comment">% set plist for filter response</span>
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235 plrsp = plist(<span class="string">'bank'</span>,<span class="string">'parallel'</span>,<span class="string">'f1'</span>,1e-5,<span class="string">'f2'</span>,0.5,<span class="string">'nf'</span>,100,<span class="string">'scale'</span>,<span class="string">'log'</span>);
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236
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237 <span class="comment">% compute the response of the original noise-shape filter</span>
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Daniele Nicolodi <nicolodi@science.unitn.it>
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238 rfilt = resp(filt,plrsp);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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239 rfilt.setName;
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240
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Daniele Nicolodi <nicolodi@science.unitn.it>
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241 <span class="comment">% compute the response of our fitted filter bank</span>
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Daniele Nicolodi <nicolodi@science.unitn.it>
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242 rfobj = resp(fobj.filters,plrsp);
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243 rfobj.setName;
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244
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245 <span class="comment">% compare the responses</span>
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246 iplot(rfilt,rfobj)
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247
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248 <span class="comment">% and the percentage error on the magnitude</span>
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Daniele Nicolodi <nicolodi@science.unitn.it>
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249 pdiff = 100.*abs((rfobj-rfilt)./rfilt);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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250 pdiff.simplifyYunits;
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251 iplot(pdiff,plist(<span class="string">'YRanges'</span>,[1e-2 100]))
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252 </pre></div>
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253 <p>
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254 The first plot shows the response of the original filter and the fitted filter bank,
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Daniele Nicolodi <nicolodi@science.unitn.it>
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255 whereas the second plot reports the percentage difference between fitted model and target filter magnitude. <br/>
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256 As can be seen, the difference between filters magnitude is at most 10%.
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257 <div align="center">
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258 <IMG src="images/ltpda_training_1/topic5/ltpda_training_5_1_4.png" align="center" border="0">
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259 </div>
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260 <div align="center">
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261 <IMG src="images/ltpda_training_1/topic5/ltpda_training_5_1_5.png" align="center" border="0">
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262 </div>
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263 </p>
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264
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265
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266
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267
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268
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269
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270
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271
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272
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273
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274
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275
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276
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277
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278
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279
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280
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281
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282 </p>
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283
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Daniele Nicolodi <nicolodi@science.unitn.it>
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284 <br>
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285 <br>
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Daniele Nicolodi <nicolodi@science.unitn.it>
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286 <table class="nav" summary="Navigation aid" border="0" width=
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287 "100%" cellpadding="0" cellspacing="0">
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288 <tr valign="top">
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289 <td align="left" width="20"><a href="ltpda_training_topic_5.html"><img src=
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290 "b_prev.gif" border="0" align="bottom" alt=
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Daniele Nicolodi <nicolodi@science.unitn.it>
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291 "Topic 5 - Model fitting"></a>&nbsp;</td>
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292
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293 <td align="left">Topic 5 - Model fitting</td>
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294
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295 <td>&nbsp;</td>
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296
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297 <td align="right">Generation of noise with given PSD</td>
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298
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299 <td align="right" width="20"><a href=
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300 "ltpda_training_topic_5_2.html"><img src="b_next.gif" border="0" align=
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301 "bottom" alt="Generation of noise with given PSD"></a></td>
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302 </tr>
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Daniele Nicolodi <nicolodi@science.unitn.it>
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303 </table><br>
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304
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305 <p class="copy">&copy;LTP Team</p>
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
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306 </body>
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Daniele Nicolodi <nicolodi@science.unitn.it>
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