Fitting data to a model to estimate parameters using lsqcurvefit: Unrecognized function or variable error

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Özge Biçer
Özge Biçer 2023-3-7
回答： Sulaymon Eshkabilov 2025-4-13
I am trying to fit experimental data (time vs concentration) to a kinetic model using lsqcurvefit function.
t=[0 10 20 30 50 70 90 110];
GLCexp= [5698 5400 4612 3811 3514 3400 2825 2406];
% hold on
plot(t,GLCexp,'ro')
title('Data points')
% hold off
%Define the parameters in terms of one variable k:
k(1)=kgln;
k(2)=kglc;
%Define the curve as a function of the parameters k and the data t:
%GLN0=15016;
%GLCmodel=(GLN0*kgln*exp(-kgln*t))/(kglc-kgln) - (GLN0*kgln*exp(-kglc*t))/(kglc-kgln);
GLCmodel=@(k,t)(15016*k(1)*exp(-k(1)*t))/(k(2)-k(1)) - (15016*k(1)*exp(-k(2)*t))/(k(2)-k(1));
%Set initial point k0
k0 = [0.01 0.01];
%Run the solver and plot the resulting fit
[k,resnorm,residual,exitflag,output] = lsqcurvefit(GLCmodel,k0,t,GLCexp)
hold on
plot(t,GLCmodel(k,t))
hold off
When I click run it gives this error:
Unrecognized function or variable 'kgln'.
Error in datdemoGLC (line 9)
k(1)=kgln;
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Amy
Amy 2025-4-12
How I got the error for Unrecognized function or variable 'M_observed'.
Error in semilinearleastsquaremodel (line 17)
optParams = lsqcurvefit(modelFunc, initialParams, M_data, M_observed);
Torsten
Torsten 2025-4-12
From the error message it seems that an array with name "M_observed" is not defined in the part of your code where you call "lsqcurvefit".

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回答（1 个）

Sulaymon Eshkabilov
Ran in:
It looks like that you overlooked or mistyped one '-' in the model formulation of GLCmodel. Here is the complete solution:
t=[0 10 20 30 50 70 90 110];
GLCexp= [5698 5400 4612 3811 3514 3400 2825 2406];
GLCmodel=@(k,t)(15016*k(1)*exp(-k(1)*t))/(k(2)-k(1)) + (15016*k(1)*exp(-k(2)*t))/(k(2)-k(1));
%Set initial values for k0:
k0 = [.02 -.02];
% Run the solver and plot the resulting fit
[k,resnorm,residual,exitflag,output] = lsqcurvefit(GLCmodel,k0,t,GLCexp)
Local minimum possible. lsqcurvefit stopped because the final change in the sum of squares relative to its initial value is less than the value of the function tolerance.
k = 1×2
0.0029 0.0184
<mw-icon class=""></mw-icon>
<mw-icon class=""></mw-icon>
resnorm = 3.6663e+05
residual = 1×8
-0.7059 -263.1343 47.0779 439.3891 83.0787 -292.5278 -91.6579 35.1723
<mw-icon class=""></mw-icon>
<mw-icon class=""></mw-icon>
exitflag = 3
output = struct with fields:
firstorderopt: 98.4302 iterations: 20 funcCount: 63 cgiterations: 0 algorithm: 'trust-region-reflective' stepsize: 1.3700e-07 message: 'Local minimum possible....' bestfeasible: [] constrviolation: []
figure
plot(t,GLCexp,'rd', 'MarkerFaceColor', 'y', 'MarkerSize', 9)
title('Data points')
hold on
plot(t,GLCmodel(k,t), 'b-', 'LineWidth',2)
hold off
grid on
legend('Data', 'Fit Model')
xlabel('Time')
ylabel('GLCexp')
fprintf('Found Fit Model Coefficients are: k = [%1.5f %1.5f] \n', k)
Found Fit Model Coefficients are: k = [0.00293 0.01838]
disp('Found Fit model is: ')
Found Fit model is:
fprintf('%1.5f*exp(-%1.5f*t)/%1.5f + %1.5f*exp(-%1.5f*t)/%1.5f \n',...
[15016*k(1), k(1), k(2)-k(1), 15016*k(1), k(1), k(2)-k(1)])
43.99815*exp(-0.00293*t)/0.01545 + 43.99815*exp(-0.00293*t)/0.01545

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