LSQCURVEFIT Error When Trying To Optimize Parameters
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I am trying to optimize 5 parameters using lsqcurvefite. I keep getting "Array indices must be positive integers or logical values". "Error in @(c,xdata) c(0)+c(1)..." line. I know my two vectors/data (VarName1 and VarName2 attached) are not positive integers, but that is my data for which I am trying obtain c0, c1, c2, c3, and c4 so that I can then fit my data with the best curve. How can I do nonlinear least squares fitting when most data will not be integers and not necessarily positive?
xdata = VarName1;
ydata = VarName2;
predicted = @(c,xdata) c(0) + c(1)*cos(xdata) + c(2)*cos(xdata) + c(3)*cos(xdata) + c(4)*cos(xdata);
c0 = [-276.8401; -0.01386; 0.007231; 0.011035; 0.001128];
[ahat,resnorm,residual,exitflag,output,lambda,jacobian] = ...
lsqcurvefit(predicted,c0,xdata,ydata);
1 个评论
Kelly McGuire
2019-2-1
编辑：Kelly McGuire
2019-2-1
采纳的回答
更多回答（1 个）
Alan Weiss
2019-2-1
0 个投票
I think that you probably made a gross modeling error, because your predicted function is the same as
predicted = @(c,xdata) c(1) + (c(2) + c(3) + c(4) + c(5))*cos(xdata);
which makes it clear that you are really fitting to
predicted = @(c,xdata) c(1) + c(2)*cos(xdata);
In other words, you are trying to fit four parameters that are indistinguishable from one parameter.
I would guess (but don't know) that you meant
predicted = @(c,xdata) c(0) + cos(c(1)*xdata) + cos(c(2)*xdata) + cos(c(3)*xdata) + cos(c(4)*xdata);
or maybe
predicted = @(c,xdata) c(0) + c(5)*cos(c(1)*xdata) + c(6)*cos(c(2)*xdata) + c(7)*cos(c(3)*xdata) + c(8)*cos(c(4)*xdata);
One more thing: the problem as your wrote it is linear in the c variables, and if you keep it linear in those variables, then you should use a linear solver (backslash or lsqlin) instead of a nonlinear solver.
Alan Weiss
MATLAB mathematical toolbox documentation
2 个评论
Kelly McGuire
2019-2-1
编辑：Kelly McGuire
2019-2-1
I am trying to use this function: 
with n=4. I don't know what happened during copy and paste, but that wasn't the correct equation. Also, I tried lsqlin, but I get "too many ouput arguments" error. I typed c in the command window, and it gave values for c, but plugging them into the equation still doesn't give a good fit. This is what my code above should have had (it's corrected above):
with n=4. I don't know what happened during copy and paste, but that wasn't the correct equation. Also, I tried lsqlin, but I get "too many ouput arguments" error. I typed c in the command window, and it gave values for c, but plugging them into the equation still doesn't give a good fit. This is what my code above should have had (it's corrected above):predicted = @(c,xdata) c(1) + c(2)*cos(xdata) + c(3)*(cos(xdata)).^2 + c(4)*(cos(xdata)).^3 + c(5)*(cos(xdata)).^4;
Alex Sha
2020-3-6
Hi, Kelly, the fellow result can be get wiyhout effort, looks perfect:
Root of Mean Square Error (RMSE): 7.26715070782157E-5
Sum of Squared Residual: 1.00341810879364E-7
Correlation Coef. (R): 0.999871461575304
R-Square: 0.999742939672734
Adjusted R-Square: 0.999710807131826
Parameter Best Estimate
---------- -------------
c1 -276.838397020957
c2 -0.00624011143593994
c3 0.00535610180032774
c4 0.014121146620983
c5 0.000862234247990687
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