Jim,

most if not all fitting strategies make use of an assumption concerning the
underlying model.

For those who are not sure what the underlying model is this one

http://creativemachines.cornell.edu/eureqa

is the hottest tool that I have ever seen. Give it a try.

Best regards

Ulrich

> -----Ursprungliche Nachricht-----
> Von: [email protected] 
> [mailto:[email protected]] Im Auftrag von Jim Lux
> Gesendet: Freitag, 4. Oktober 2013 19:38
> An: Discussion of precise time and frequency measurement
> Betreff: [time-nuts] exponential+linear fit
> 
> 
> I'm trying to find a good way to do a combination 
> exponential/linear fit 
> (for baseline removal).  It's modeling phase for a moving 
> source plus a 
> thermal transient, so the underlying physics is the linear term (the 
> phase varies linearly with time, since the velocity is constant) plus 
> the temperature effect.
> 
> the general equation is y(t) = k1 + k2*t + k3*exp(k4*t)
> 
> Working in matlab/octave, but that's just the tool, I'm 
> looking for some 
> numerical analysis insight.
> 
> I could do it in steps.. do a straight line to get k1 and k2, 
> then fit 
> k3& k4 to the residual; or fit the exponential first, then do the 
> straight line., but I'm not sure that will minimize the 
> error, or if it 
> matches the underlying model (a combination of a linear trend and 
> thermal effects) as well.
> 
> I suppose I could do something like do the fit on the 
> derivative, which 
> would be
> 
> y'(t) = k2 + k3*k4*exp(k4*t)
> 
> Then solve for the the k1.  In reality, I don't think I care as much 
> what the numbers are (particularly the k1 DC offset) so  
> could probably 
> just integrate (numerically)
> 
> y'()-k2-k3*k4*exp(k4*t) and get my sequence with the DC term, linear 
> drift, and exponential component removed.
> 
> 
> The fear I have is that differentiating emphasizes noise. 
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