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<div class="moz-cite-prefix">Hi Federico</div>
<div class="moz-cite-prefix"><br>
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<div class="moz-cite-prefix"><font face="Courier New">>It is 1)
the difficult part.</font></div>
<div class="moz-cite-prefix"><br>
</div>
<div class="moz-cite-prefix">Is it allowed to give answer outside of
Scilab? I have successfully used Curve Expert for finding the most
suitable analytical function.<br>
</div>
<div class="moz-cite-prefix"><br>
</div>
<div class="moz-cite-prefix"><a href="https://www.curveexpert.net/">https://www.curveexpert.net/</a></div>
<div class="moz-cite-prefix"><br>
</div>
<div class="moz-cite-prefix">... highly recommended.<br>
</div>
<div class="moz-cite-prefix"><br>
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<div class="moz-cite-prefix">Best regards,</div>
<div class="moz-cite-prefix">Claus</div>
<div class="moz-cite-prefix"><br>
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<div class="moz-cite-prefix">On 24.09.2019 02:50, Federico Miyara
wrote:<br>
</div>
<blockquote type="cite"
cite="mid:be0ab603-9832-e7ed-b448-4cb4cb5d455a@fceia.unr.edu.ar">
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<font face="Courier New">Dear all,<br>
<br>
This is a tough one, and I'm aware it may not be directly
related to Scilab, so sorry if it is off-topic. I post it anyway
because it could lead in a future to a toolbox.<br>
<br>
I'm looking for general methods for obtaining analytic models
for empirical data such as a variable which depends on two
independent variables (i.e., a double entry table). The aim
isn't just to interpolate but to find a sort of analytical law
that approximates the data. This has two steps:<br>
<br>
1) Finding an analytic function, of two independent variables
with as few parameters as possible that can likely approximate
the data.<br>
<br>
2) Optimizing the parameters for the best approximation to the
data<br>
<br>
2) Is relatively easy by least-squares or other optimization
techniques. It is 1) the difficult part.<br>
<br>
Any help as to literature or known mehods will be welcome<br>
<br>
Federico Miyara <br>
<br>
<br>
<br>
<br>
<br>
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