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Effacer les filtres

lsqcurvefit not adjusting some parameters as expected

19 vues (au cours des 30 derniers jours)
Leor Greenberger
Leor Greenberger le 26 Juil 2019
I am trying to fit some data to a 3 parameter curve expressed as .
What I am finding when using lsqcurvefit is that it converges on what appears to be good values for and but not for c. In fact, c remains unchanged from the guessed value, regardless of its initial value.
First I try with c = p(3) = 1000
x = 0:2047;
y = load('y.mat'); % see attachment
fun = @(p,x)abs(p(1)*(x-p(3))+p(2)*(x-p(3)).^3);
options = optimoptions(@lsqcurvefit,'StepTolerance',1e-10, 'Display', 'iter-detailed', 'FunctionTolerance', 1E-12);
pGuess = [2.5E-5 2.6E-12 1000];
[p,fminres] = lsqcurvefit(fun,pGuess,x,y, [], [], options)
Norm of First-order
Iteration Func-count f(x) step optimality
0 4 0.00635297 1.03e+08
1 8 0.00632173 3.02663e-13 99.9
2 12 0.00623569 9.85058e-05 1.13e+04
3 16 0.00623569 3.31307e-17 0.0114
Optimization stopped because the relative sum of squares (r) is changing
by less than options.FunctionTolerance = 1.000000e-12.
p =
2.58614068198555e-05 1.75916049599052e-12 1000.00009850203
fminres =
0.00623568562340633
Now I try with c = p(3) = 1400:
pGuess = [2.5E-5 2.6E-12 1400];
[p,fminres] = lsqcurvefit(fun,pGuess,x,y, [], [], options)
Norm of First-order
Iteration Func-count f(x) step optimality
0 4 0.168905 9.79e+09
1 8 0.10571 6.45539e-12 1.16e+06
2 12 0.105681 9.10932e-06 1.43e+08
3 16 0.105681 0.000231759 1.43e+08
4 20 0.105681 5.79397e-05 1.43e+08
5 24 0.105681 1.44849e-05 1.43e+08
6 28 0.105681 3.62123e-06 1.43e+08
7 32 0.105681 9.05308e-07 1.43e+08
8 36 0.105681 2.26327e-07 1.43e+08
9 40 0.105681 5.65817e-08 1.43e+08
10 44 0.105681 1.41454e-08 1.43e+08
11 48 0.105681 3.53636e-09 1.43e+08
12 52 0.105681 8.8409e-10 1.43e+08
13 56 0.105681 2.21022e-10 1.43e+08
14 60 0.105681 5.52556e-11 1.43e+08
Optimization stopped because the norm of the current step, 5.525560e-11,
is less than options.StepTolerance = 1.000000e-10.
p =
2.4936115641411e-05 -3.9025705054523e-12 1399.9999908909
fminres =
0.105680830464589
From the data set it is clear that the min occurs at x = 1066.
>> [m,k] = min(y)
m =
0.000292016392169768
k =
1067
>> x(k)
ans =
1066
  3 commentaires
Leor Greenberger
Leor Greenberger le 26 Juil 2019
I knew I forgot something! It is attached now. Thanks for looking into this!
Walter Roberson
Walter Roberson le 27 Juil 2019
Your function would benefit from a constraint.
fun = @(p,x)abs(p(1)*(x-p(3))+p(2)*(x-p(3)).^3);
if you feed in -p(1) and -p(2) then you the result will have the same abs() as original p(1) and p(2) . Therefore you can constrain one of the values, such as p(1) to be >= 0, which will reduce searching.

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Réponse acceptée

Matt J
Matt J le 27 Juil 2019
It helps to pre-normalize your x,y data and to use polyfit to generate a smart initial guess,
%data pre-normalization
y=y.'/max(y);
[~,imin]=min(y);
x=(x-x(imin))/max(x);
%generate initial guess
p0=polyfit(x(imin:end),y(imin:end),3);
pGuess = [p0(1),p0(3), 0];
fun = @(p,x)abs(p(1)*(x-p(3))+p(2)*(x-p(3)).^3);
options = optimoptions(@lsqcurvefit,'StepTolerance',1e-10, 'Display', 'iter-detailed', 'FunctionTolerance', 1E-12);
[p,fminres,~,ef] = lsqcurvefit(fun,pGuess,x,y, [], [], options)
plot(x,y,'o-',x(1:20:end),fun(p,x(1:20:end)),'x--y'); shg
untitled.png
  3 commentaires
Leor Greenberger
Leor Greenberger le 27 Juil 2019
Modifié(e) : Leor Greenberger le 27 Juil 2019
Thank you very much for helping with this! The normalization you did is interesting. Ultimately what I need to do is create an algorithm that can find the inflection point with the least number of data samples. That is, I have a digital system with an 11 bit register and what you see here is my characterization of it from code 0 to 2047. I set the register and measured the output of the system. I wonder if this will work if I have enough samples and guess min(y).
Matt J
Matt J le 27 Juil 2019
You're welcome, but please Accept-click the answer if you are satisfied that the fitting code is now working.

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Plus de réponses (1)

Alex Sha
Alex Sha le 22 Fév 2020
The best solution seems to be:
Root of Mean Square Error (RMSE): 8.58712889537096E-5
Sum of Squared Residual: 1.50943288116718E-5
Correlation Coef. (R): 0.999947601349384
R-Square: 0.999895205444387
Adjusted R-Square: 0.999895102905683
Determination Coef. (DC): 0.99989229572485
Chi-Square: 0.00435400203642259
F-Statistic: 9515701.1004098
Parameter Best Estimate
---------- -------------
p1 -2.54821313582336E-5
p2 -2.52958254532916E-12
p3 1062.58024839597

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