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author Daniele Nicolodi <nicolodi@science.unitn.it>
date Wed, 23 Nov 2011 19:22:13 +0100
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11 <title>Iterative linear parameter estimation for multichannel systems - symbolic system model in frequency domain (LTPDA Toolbox)</title>
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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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29 "sigproc_example_matrix_linlsqsvd.html"><img src="b_prev.gif" border="0" align=
30 "bottom" alt="Linear least squares with singular value deconposition - multiple experiments"></a>&nbsp;&nbsp;&nbsp;<a href=
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32 "bottom" alt="Iterative linear parameter estimation for multichannel systems - ssm system model in time domain"></a></td>
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35
36 <h1 class="title"><a name="f3-12899" id="f3-12899"></a>Iterative linear parameter estimation for multichannel systems - symbolic system model in frequency domain</h1>
37 <hr>
38
39 <p>
40
41 <p>Iterative linear parameter estimation for multichannel systems - symbolic system model in frequency domain.</p>
42
43 <h2>Contents</h2>
44 <ul>
45 <li><a href="#1">set plist for retriving</a></li>
46 <li><a href="#2">retrive data</a></li>
47 <li><a href="#3">Load input signal</a></li>
48 <li><a href="#4">load Whitening filters</a></li>
49 <li><a href="#6">Build input objects</a></li>
50 <li><a href="#7">system model 1</a></li>
51 <li><a href="#8">Do Fit</a></li>
52 <li><a href="#9">system model 2</a></li>
53 <li><a href="#10">Set Model Alias</a></li>
54 <li><a href="#11">Do fit with alias</a></li>
55 </ul>
56
57 <h2>set plist for retriving<a name="1"></a></h2>
58
59 <div class="fragment"><pre>
60
61 pl = plist(<span class="string">'hostname'</span>, <span class="string">'lpsdas01.esac.esa.int'</span>, <span class="string">'database'</span>, <span class="string">'ex6'</span>);
62 </pre></div>
63
64 <h2>retrive data<a name="2"></a></h2>
65
66 <div class="fragment"><pre>
67 o1_1 = ao(pl.pset(<span class="string">'binary'</span>, <span class="string">'yes'</span>, <span class="string">'id'</span>, 169));
68 o12_1 = ao(pl.pset(<span class="string">'binary'</span>, <span class="string">'yes'</span>, <span class="string">'id'</span>, 170));
69
70 o1_2 = ao(pl.pset(<span class="string">'binary'</span>, <span class="string">'yes'</span>, <span class="string">'id'</span>, 171));
71 o12_2 = ao(pl.pset(<span class="string">'binary'</span>, <span class="string">'yes'</span>, <span class="string">'id'</span>, 172));
72 </pre></div>
73
74 <h2>Load input signal<a name="3"></a></h2>
75
76 <div class="fragment"><pre>
77 is1 = matrix(pl.pset(<span class="string">'binary'</span>, <span class="string">'yes'</span>, <span class="string">'id'</span>, 173));
78 is2 = matrix(pl.pset(<span class="string">'binary'</span>, <span class="string">'yes'</span>, <span class="string">'id'</span>, 180));
79 </pre></div>
80
81 <h2>load Whitening filters<a name="4"></a></h2>
82 <span class="comment">% Stoc filter</span>
83
84 <div class="fragment"><pre>
85 fil1 = filterbank(pl.pset(<span class="string">'binary'</span>, <span class="string">'yes'</span>, <span class="string">'id'</span>, 191));
86 fil2 = filterbank(pl.pset(<span class="string">'binary'</span>, <span class="string">'yes'</span>, <span class="string">'id'</span>, 192));
87 fil3 = filterbank(miir());
88 <span class="comment">% build matrix</span>
89 wf = matrix(fil1,fil3,fil3,fil2,plist(<span class="string">'shape'</span>,[2 2]));
90 </pre></div>
91
92 <h2>Build input objects<a name="6"></a></h2>
93
94 <div class="fragment"><pre>
95
96 <span class="comment">% empty ao</span>
97 eao = ao();
98
99 <span class="comment">% exp_3_1</span>
100 os1 = matrix(o1_1,o12_1,plist(<span class="string">'shape'</span>,[2 1]));
101
102 <span class="comment">% exp_3_2</span>
103 os2 = matrix(o1_2,o12_2,plist(<span class="string">'shape'</span>,[2 1]));
104
105 <span class="comment">% Input signals</span>
106 iS = collection(is1,is2);
107
108 <span class="comment">% Fit Params</span>
109 usedparams = {<span class="string">'A1'</span>,<span class="string">'A2'</span>,<span class="string">'S21'</span>,<span class="string">'w1'</span>,<span class="string">'w12'</span>,<span class="string">'del1'</span>,<span class="string">'del2'</span>};
110
111 nsecs = os1.objs(1).data.nsecs;
112 fs = os1.objs(1).data.fs;
113 npad = nsecs*fs;
114
115 <span class="comment">% set bounded params</span>
116 bdparams = {<span class="string">'del1'</span>,<span class="string">'del2'</span>};
117 bdvals = {[0.1 0.3],[0.1 0.3]};
118
119 </pre></div>
120
121 <h2>system model 1<a name="7"></a></h2>
122
123 <div class="fragment"><pre>
124
125 H = matrix(plist(<span class="string">'built-in'</span>,<span class="string">'ifo2ifo'</span>, <span class="string">'Version'</span>, <span class="string">'LSS v4.9.2 Phys Params'</span>));
126 </pre></div>
127
128 <h2>Do Fit<a name="8"></a></h2>
129
130 <div class="fragment"><pre>
131
132 plfit = plist(<span class="keyword">...</span>
133 <span class="string">'FitParams'</span>,usedparams,<span class="keyword">...</span>
134 <span class="string">'Model'</span>,H,<span class="keyword">...</span>
135 <span class="string">'Input'</span>,iS,<span class="keyword">...</span>
136 <span class="string">'WhiteningFilter'</span>,wf,<span class="keyword">...</span>
137 <span class="string">'tol'</span>,1,<span class="keyword">...</span>
138 <span class="string">'Nloops'</span>,10,<span class="keyword">...</span>
139 <span class="string">'Npad'</span>,npad,<span class="keyword">...</span>
140 <span class="string">'Ncut'</span>,1e4);
141
142 opars1 = linfitsvd(os1,os2,plfit);
143 </pre></div>
144
145 <h2>system model 2<a name="9"></a></h2>
146
147 <div class="fragment"><pre>
148
149 H2 = matrix(plist(<span class="string">'built-in'</span>,<span class="string">'ifo2ifo'</span>, <span class="string">'Version'</span>, <span class="string">'LSS v4.9.2 Phys Params Alias'</span>));
150 </pre></div>
151
152 <h2>Set Model Alias<a name="10"></a></h2>
153
154 <div class="fragment"><pre>
155
156 plalias = plist(<span class="string">'nsecs'</span>,nsecs,<span class="string">'npad'</span>,npad,<span class="string">'fs'</span>,fs);
157 <span class="keyword">for</span> ii=1:numel(H2.objs)
158 H2.objs(ii).assignalias(H2.objs(ii),plalias);
159 <span class="keyword">end</span>
160 </pre></div>
161
162 <h2>Do fit with alias<a name="11"></a></h2>
163
164 <div class="fragment"><pre>
165
166 plfit2 = plist(<span class="keyword">...</span>
167 <span class="string">'FitParams'</span>,usedparams,<span class="keyword">...</span>
168 <span class="string">'Model'</span>,H2,<span class="keyword">...</span>
169 <span class="string">'BoundedParams'</span>,bdparams,<span class="keyword">...</span>
170 <span class="string">'BoundVals'</span>,bdvals,<span class="keyword">...</span>
171 <span class="string">'Input'</span>,iS,<span class="keyword">...</span>
172 <span class="string">'WhiteningFilter'</span>,wf,<span class="keyword">...</span>
173 <span class="string">'tol'</span>,1,<span class="keyword">...</span>
174 <span class="string">'Nloops'</span>,10,<span class="keyword">...</span><span class="comment"> % maximum number of fit iterations</span>
175 <span class="string">'Npad'</span>,npad,<span class="keyword">...</span>
176 <span class="string">'Ncut'</span>,1e4); <span class="comment">% number of data points to skip at the starting of the series to avoid whitening filter transient</span>
177
178 opars2 = linfitsvd(os1,os2,plfit2);
179 </pre></div>
180
181
182 </p>
183
184 <br>
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186 <table class="nav" summary="Navigation aid" border="0" width=
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189 <td align="left" width="20"><a href="sigproc_example_matrix_linlsqsvd.html"><img src=
190 "b_prev.gif" border="0" align="bottom" alt=
191 "Linear least squares with singular value deconposition - multiple experiments"></a>&nbsp;</td>
192
193 <td align="left">Linear least squares with singular value deconposition - multiple experiments</td>
194
195 <td>&nbsp;</td>
196
197 <td align="right">Iterative linear parameter estimation for multichannel systems - ssm system model in time domain</td>
198
199 <td align="right" width="20"><a href=
200 "sigproc_example_matrix_linfitsvd_ssm.html"><img src="b_next.gif" border="0" align=
201 "bottom" alt="Iterative linear parameter estimation for multichannel systems - ssm system model in time domain"></a></td>
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203 </table><br>
204
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