Error using lsqcurvefit (line 271) Function value and YDATA sizes are not equal.

Question

srcn 2021-9-14

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1453034-error-using-lsqcurvefit-line-271-function-value-and-ydata-sizes-are-not-equal

评论： Star Strider 2021-9-22

Re-analyze-2.xlsx

Hello everybody. I am new to MATLAB and I'm trying to find the parameters to best fit a model function having experimental points as ti and yi. The main code and functions are:

data = xlsread("Re-analyze.xlsx");

ti = data(:,6);

yi = data(:,7);

R = data(1,1);

T = data(2,1);

Me = data(3,1);

Mn0i = data(4,1);

Vs0 = data(6,1);

Mse = data(7,1);

V0 = data(8,1);

A=((36*pi)^(1/3))*((V0)^(2/3));

X0=[0.72; 28.876; 6568.86;1;1];

save variables_LpPsVb R T A Me V0 A Me Mn0i Vs0 Mse X0

function:

function X = Lp_Ps_Vb (b,t)

load variables_LpPsVb R T A V0 Me Mn0i Vs0 Mse

% % % b(1:3) = Parameters, b(4:5) = Initial Conditions

% % % MAPPING: x(1) = x, x(2) = y, b(1) = Lp, b(2) = Ps, b(3) = Vb

Ddv_Div = @(t,x,b)[((-b(1)*x(1)*A*R*T*Me*Vs0)+ b(1)*x(2)*R*T*A+b(1)*A*R*T*Mn0i*V0*Vs0-b(1)*b(3)*A*R*T*Mn0i*Vs0)/(Vs0*x(1));

(b(2)*x(1)*A*Vs0*Mse*Vs0-b(2)*x(2)*A*Vs0)/(Vs0*x(1))];

[~,X] = ode45(@(t,x) Ddv_Div(t,x,b(1:3)), t, b(4:5));

end

I get this error message:

Error using lsqcurvefit (line 271) Function value and YDATA sizes are not equal.

Unfortunately, I can not see my mistake. What should I try to solve it?

Thank you in advance!

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Matt J 2021-9-18

@srcn The error is thrown by lsqcurvefit, but you haven't posted the code where lsqcurvefit is called - we can't see how it's being used.

srcn 2021-9-18

so sorry. I wrote it in the comments below but forgot to add it here.

You can find corrected code : updated code

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Answer 1

Star Strider 2021-9-15

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此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1453034-error-using-lsqcurvefit-line-271-function-value-and-ydata-sizes-are-not-equal#answer_788009

The ‘Lp_Ps_Vb’ function is going to return ‘X’ that is going to be a (numel(t) x 2) matrix, so lsqcurvefit needs to have a matching matrix of data to compare to it. (The error that was thrown can also occur when rows of data are compared to columns returned by the objective function, or the reverse, however I doubt that is the problem here.)

I am not certain what you are doing, however if you want to fit the system of differential equations to your data, the approach in Coefficient estimation for a system of coupled ODEs will likely work.

It will be necessary to adapt that code to your system. That should not be difficult.

.

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Star Strider 2021-9-16

在 MATLAB Online 中打开

There are several problems with the data that it will be necessary for you to correct before I go further (if that is even possible). Also, the lsqcurvefit call is missing, so I have no idea what you are doing with it.

T1 = readtable('https://www.mathworks.com/matlabcentral/answers/uploaded_files/738659/Re-analyze-2.xlsx', 'VariableNamingRule','preserve')
T1 = 28×9 table
Var1parametersVar3Var4Var5Var6Var7t (min)V (r.u.)________________________________________________________________________________________

    {0×0 char}    {'R               ='}    8.2057e+13    {'(atm*um3/mol*K)'}    NaN     NaN     NaN            0          1 
    {0×0 char}    {'T               ='}        295.15    {'(K)'            }    NaN     NaN     NaN     0.083333    0.93461 
    {0×0 char}    {'Me           ='   }       1.8e-15    {'(mol/um3)'      }    NaN     NaN     NaN      0.16667    0.79709 
    {0×0 char}    {'Mn0i       ='     }         3e-16    {'(mol/um3)'      }    NaN     NaN     NaN         0.25    0.73734 
    {0×0 char}    {'Vbf          ='   }          0.01    {'(-)'            }    NaN     NaN     NaN      0.33333    0.72288 
    {0×0 char}    {'Vs0          ='   }    7.1001e+13    {'(um3/mol)'      }    NaN     NaN     NaN      0.41667    0.68757 
    {0×0 char}    {'Mse         ='    }       1.5e-15    {'(mol/um3)'      }    NaN     NaN     NaN          0.5    0.67377 
    {0×0 char}    {'V0            ='  }    6.5689e+05    {'(µm)'           }    NaN     NaN     NaN      0.58333    0.66694 
    {0×0 char}    {'Vend     ='       }    5.9455e+05    {'(µm)'           }    NaN     NaN     NaN      0.66667     0.6772 
    {0×0 char}    {0×0 char           }           NaN    {0×0 char         }    NaN     NaN     NaN         0.75     0.6772 
    {0×0 char}    {0×0 char           }           NaN    {0×0 char         }    NaN     NaN     NaN      0.83333    0.68757 
    {0×0 char}    {0×0 char           }           NaN    {0×0 char         }    NaN     NaN     NaN      0.91667    0.68757 
    {0×0 char}    {0×0 char           }           NaN    {0×0 char         }    NaN     NaN     NaN            1    0.69803 
    {0×0 char}    {0×0 char           }           NaN    {0×0 char         }    NaN     NaN     NaN       1.1667    0.71216 
    {0×0 char}    {0×0 char           }           NaN    {0×0 char         }    NaN     NaN     NaN       1.4167    0.71929 
    {0×0 char}    {0×0 char           }           NaN    {0×0 char         }    NaN     NaN     NaN       1.6667    0.74098 
data = readmatrix('https://www.mathworks.com/matlabcentral/answers/uploaded_files/738659/Re-analyze-2.xlsx')
data = 28×9
	1.0e+13 *

       NaN       NaN    8.2057       NaN       NaN       NaN       NaN         0    0.0000
       NaN       NaN    0.0000       NaN       NaN       NaN       NaN    0.0000    0.0000
       NaN       NaN    0.0000       NaN       NaN       NaN       NaN    0.0000    0.0000
       NaN       NaN    0.0000       NaN       NaN       NaN       NaN    0.0000    0.0000
       NaN       NaN    0.0000       NaN       NaN       NaN       NaN    0.0000    0.0000
       NaN       NaN    7.1001       NaN       NaN       NaN       NaN    0.0000    0.0000
       NaN       NaN    0.0000       NaN       NaN       NaN       NaN    0.0000    0.0000
       NaN       NaN    0.0000       NaN       NaN       NaN       NaN    0.0000    0.0000
       NaN       NaN    0.0000       NaN       NaN       NaN       NaN    0.0000    0.0000
       NaN       NaN       NaN       NaN       NaN       NaN       NaN    0.0000    0.0000
ti = data(:,6)
ti = 28×1
   NaN
   NaN
   NaN
   NaN
   NaN
   NaN
   NaN
   NaN
   NaN
   NaN
yi = data(:,7)
yi = 28×1
   NaN
   NaN
   NaN
   NaN
   NaN
   NaN
   NaN
   NaN
   NaN
   NaN
R = data(1,1)
R = NaN
T = data(2,1)
T = NaN
Me = data(3,1)
Me = NaN
Mn0i = data(4,1)
Mn0i = NaN
Vs0 = data(6,1)
Vs0 = NaN
Mse = data(7,1)
Mse = NaN
V0 = data(8,1)
V0 = NaN
A=((36*pi)^(1/3))*((V0)^(2/3))
A = NaN
X0=[0.72; 28.876; 6568.86;1;1]
X0 = 5×1
	1.0e+03 *

    0.0007
    0.0289
    6.5689
    0.0010
    0.0010

It is not possible to use xlsread with the online Run feature, however that should not be a problem with respect to reading the data.

.

srcn 2021-9-16

Sorry, my bad. I updated the code.

data = [0 1

0.083333333 0.934614298

0.166666667 0.797092293

0.25 0.737335644

0.333333333 0.722878919

0.416666667 0.687565423

0.5 0.673768943

0.583333333 0.666939375

0.666666667 0.677201142

0.75 0.677201142

0.833333333 0.687565423

0.916666667 0.687565423

1 0.698034915

1.166666667 0.712161837

1.416666667 0.719293938

1.666666667 0.740980274

1.916666667 0.755676543

2.166666667 0.774319095

2.416666667 0.793265762

2.666666667 0.80093111

2.916666667 0.820307685

3.166666667 0.832081278

3.416666667 0.836031538

3.666666667 0.859993354

3.916666667 0.872142776

4.166666667 0.888520135

4.416666667 0.900938784

4.666666667 0.905101247

];

ti = data(:,1);

yi = data(:,2);

R = 8.20575E+13;

T = 295.15;

Me = 1.8E-15;

Mn0i = 3E-16;

Vs0 = 7.10015E+13;

Mse = 1.5E-15;

V0 = 656886.1948;

A=((36*pi)^(1/3))*((V0)^(2/3));

X0=[0.72; 28.876; 6568.86;1;1];

save variables R T A Me V0 A Me Mn0i Vs0 Mse ti yi

function:

function X = Lp_Ps_Vb (b,t)

load variables_LpPsVb R T A V0 Me Mn0i Vs0 Mse

% % % b(1:3) = Parameters, b(4:5) = Initial Conditions

% % % MAPPING: x(1) = x, x(2) = y, b(1) = Lp, b(2) = Ps, b(3) = Vb

Ddv_Div = @(t,x,b)[((-b(1)*x(1)*A*R*T*Me*Vs0)+ b(1)*x(2)*R*T*A+b(1)*A*R*T*Mn0i*V0*Vs0-b(1)*b(3)*A*R*T*Mn0i*Vs0)/(Vs0*x(1));

(b(2)*x(1)*A*Vs0*Mse*Vs0-b(2)*x(2)*A*Vs0)/(Vs0*x(1))];

%Ddv_Div = @(t,x,b)[-b(1)*A*R*T*(Me-x(2)/(Vs0*x(1))-Mn0i*(V0-b(3))/x(1)); Vs0*b(2)*A*(Mse-x(2)/(Vs0*x(1)))];

[~,X] = ode45(@(t,x) Ddv_Div(t,x,b(1:3)), t, b(4:5));

end

lsqcurvefit call :

B = lsqcurvefit (@Lp_Ps_Vb, X0, ti, yi, zeros(size(X0)), inf(size(X0)));

printf(1, '\n\tParameters:\n\t\tLp = %11.3E\n\t\tPs = %11.3E\n\t\tVb = %11.3E\n\tInitial Conditions:\n\t\tx = %11.3E\n\t\ty = %11.3E\n', B);

Ddv_Div = @(t,x,b)[((-b(1)*x(1)*A*R*T*Me*Vs0)+ b(1)*x(2)*R*T*A+b(1)*A*R*T*Mn0i*V0*Vs0-b(1)*b(3)*A*R*T*Mn0i*Vs0)/(Vs0*x(1));

(b(2)*x(1)*A*Vs0*Mse*Vs0-b(2)*x(2)*A*Vs0)/(Vs0*x(1))];

Star Strider 2021-9-18

在 MATLAB Online 中打开

The problem is that the ‘Lp_Ps_Vb’ function returns two columns, however the fit is to one column of data. I created ‘Xr’ as the column to return (and arbitrarily chose the first column of ‘X’). Choose whatever column works.

This now runs, however it timed out in the online Run feature before it converged, so I was not able to estimate parameters for it here.

data = [0	1
0.083333333	0.934614298
0.166666667	0.797092293
0.25	0.737335644
0.333333333	0.722878919
0.416666667	0.687565423
0.5	0.673768943
0.583333333	0.666939375
0.666666667	0.677201142
0.75	0.677201142
0.833333333	0.687565423
0.916666667	0.687565423
1	0.698034915
1.166666667	0.712161837
1.416666667	0.719293938
1.666666667	0.740980274
1.916666667	0.755676543
2.166666667	0.774319095
2.416666667	0.793265762
2.666666667	0.80093111
2.916666667	0.820307685
3.166666667	0.832081278
3.416666667	0.836031538
3.666666667	0.859993354
3.916666667	0.872142776
4.166666667	0.888520135
4.416666667	0.900938784
4.666666667	0.905101247];
ti = data(:,1);
yi = data(:,2);
R = 8.20575E+13;
T = 295.15;
Me = 1.8E-15;
Mn0i = 3E-16;
Vs0 = 7.10015E+13;
Mse = 1.5E-15;
V0 = 656886.1948;
A=((36*pi)^(1/3))*((V0)^(2/3));
X0=[0.72; 28.876; 6568.86; 1; 1];
B = lsqcurvefit(@(b,t)Lp_Ps_Vb(b,t,R,T,Me,Mn0i,Vs0,Mse,V0,A), X0, ti, yi, zeros(size(X0)), inf(size(X0)));
fprintf(1, '\n\tParameters:\n\t\tLp = %11.3E\n\t\tPs = %11.3E\n\t\tVb = %11.3E\n\tInitial Conditions:\n\t\tx = %11.3E\n\t\ty = %11.3E\n', B);
% fprintf(1,'\tRate Constants:\n')
% for k1 = 1:length(B)
%     fprintf(1, '\t\tTheta(%d) = %14.11f\n', k1, B(k1))
% end
tv = linspace(min(ti), max(ti));
Cfit = Lp_Ps_Vb(B,tv,R,T,Me,Mn0i,Vs0,Mse,V0,A);
figure
hd = plot(ti, yi, 'p');
for k1 = 1:size(c,2)
    CV(k1,:) = hd(k1).Color;
    hd(k1).MarkerFaceColor = CV(k1,:);
end
hold on
hlp = plot(tv, Cfit);
for k1 = 1:size(c,2)
    hlp(k1).Color = CV(k1,:);
end
hold off
grid
xlabel('Time')
ylabel('Concentration')
legend(hlp, 'C_1(t)', 'Location','best')
function Xr = Lp_Ps_Vb(b,t,R,T,Me,Mn0i,Vs0,Mse,V0,A)
% load variables_LpPsVb R T A V0 Me Mn0i Vs0 Mse
      % % % b(1:3) = Parameters,  b(4:5) = Initial Conditions
      % % % MAPPING: x(1) = x,  x(2) = y,  b(1) = Lp,  b(2) = Ps,  b(3) = Vb
    Ddv_Div = @(t,x)[((-b(1)*x(1)*A*R*T*Me*Vs0)+ b(1)*x(2)*R*T*A+b(1)*A*R*T*Mn0i*V0*Vs0-b(1)*b(3)*A*R*T*Mn0i*Vs0)/(Vs0*x(1));
                                    (b(2)*x(1)*A*Vs0*Mse*Vs0-b(2)*x(2)*A*Vs0)/(Vs0*x(1))]; 
    %Ddv_Div = @(t,x,b)[-b(1)*A*R*T*(Me-x(2)/(Vs0*x(1))-Mn0i*(V0-b(3))/x(1)); Vs0*b(2)*A*(Mse-x(2)/(Vs0*x(1)))];
     
    [~,X] = ode45(@(t,x) Ddv_Div(t,x), t, b(4:5))
    Xr = X(:,1)
end

Experiment to get the correct results.

.

Star Strider 2021-9-18

在 MATLAB Online 中打开

It just occurred to me that this is a ‘stiff’ system (amazing what a bit of sleep can do).

data = [0 1

0.083333333 0.934614298

0.166666667 0.797092293

0.25 0.737335644

0.333333333 0.722878919

0.416666667 0.687565423

0.5 0.673768943

0.583333333 0.666939375

0.666666667 0.677201142

0.75 0.677201142

0.833333333 0.687565423

0.916666667 0.687565423

1 0.698034915

1.166666667 0.712161837

1.416666667 0.719293938

1.666666667 0.740980274

1.916666667 0.755676543

2.166666667 0.774319095

2.416666667 0.793265762

2.666666667 0.80093111

2.916666667 0.820307685

3.166666667 0.832081278

3.416666667 0.836031538

3.666666667 0.859993354

3.916666667 0.872142776

4.166666667 0.888520135

4.416666667 0.900938784

4.666666667 0.905101247];

ti = data(:,1);

yi = data(:,2);

R = 8.20575E+13;

T = 295.15;

Me = 1.8E-15;

Mn0i = 3E-16;

Vs0 = 7.10015E+13;

Mse = 1.5E-15;

V0 = 656886.1948;

A=((36*pi)^(1/3))*((V0)^(2/3));

% X0=[0.72; 28.876; 6568.86; 1; 1];

X0 = rand(5,1)*10;

B = lsqcurvefit(@(b,t)Lp_Ps_Vb(b,t,R,T,Me,Mn0i,Vs0,Mse,V0,A), X0, ti, yi, zeros(size(X0)), inf(size(X0)));

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.

fprintf(1, '\n\tParameters:\n\t\tLp = %11.6E\n\t\tPs = %11.6E\n\t\tVb = %11.6E\n\tInitial Conditions:\n\t\tx = %11.6E\n\t\ty = %11.6E\n', B);

Parameters: Lp = 1.194088E-06 Ps = 8.321523E-06 Vb = 2.692267E+00 Initial Conditions: x = 1.562840E-01 y = 7.234029E-01

% fprintf(1,'\tRate Constants:\n')

% for k1 = 1:length(B)

% fprintf(1, '\t\tTheta(%d) = %14.11f\n', k1, B(k1))

% end

tv = linspace(min(ti), max(ti));

Cfit = Lp_Ps_Vb(B,tv,R,T,Me,Mn0i,Vs0,Mse,V0,A);

figure

hd = plot(ti, yi, 'p');

for k1 = 1:size(yi,2)

CV(k1,:) = hd(k1).Color;

hd(k1).MarkerFaceColor = CV(k1,:);

end

hold on

hlp = plot(tv, Cfit);

for k1 = 1:size(yi,2)

hlp(k1).Color = CV(k1,:);

end

hold off

grid

xlabel('Time')

ylabel('Concentration')

legend(hlp, 'C_1(t)', 'Location','best')

function Xr = Lp_Ps_Vb(b,t,R,T,Me,Mn0i,Vs0,Mse,V0,A)

% load variables_LpPsVb R T A V0 Me Mn0i Vs0 Mse

% % % b(1:3) = Parameters, b(4:5) = Initial Conditions

% % % MAPPING: x(1) = x, x(2) = y, b(1) = Lp, b(2) = Ps, b(3) = Vb

Ddv_Div = @(t,x)[((-b(1)*x(1)*A*R*T*Me*Vs0)+ b(1)*x(2)*R*T*A+b(1)*A*R*T*Mn0i*V0*Vs0-b(1)*b(3)*A*R*T*Mn0i*Vs0)/(Vs0*x(1));

(b(2)*x(1)*A*Vs0*Mse*Vs0-b(2)*x(2)*A*Vs0)/(Vs0*x(1))];

%Ddv_Div = @(t,x,b)[-b(1)*A*R*T*(Me-x(2)/(Vs0*x(1))-Mn0i*(V0-b(3))/x(1)); Vs0*b(2)*A*(Mse-x(2)/(Vs0*x(1)))];

[~,X] = ode15s(@(t,x) Ddv_Div(t,x), t, b(4:5));

Xr = X(:,2);

end

I am stil not certain what column of ‘X’ should be used to fit to the data. Once that is determined, it might be worthwhile to see if the genetic algorithm (ga) function can provide a better estimate of the parameters. In my experience, ga generally works better than GlobalSearch (that I also like and is best suited for some other problems) for these sorts of problems. There are a number of others it would be worthwhile experimenting with as well.

Also, be certain thatt ‘Ddv_Div’ is coded correctly, since it does not appear to be approximating the data. (I have no idea what it is, so I cannot check it for errors.)

In any event, with the solver change, this now works and produces reasonable results in a reasonable time.

.

srcn 2021-9-22

Hi Star Strider,

It took me a long time to answer because I was doing experiments to use in this analysis.

I made a few additions and modified the code. Now it is working somehow.

I really do appreciate your wisdom on all of this. Thank you for all you have done.

Star Strider 2021-9-22

As always, my pleasure!

.

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Error using lsqcurvefit (line 271) Function value and YDATA sizes are not equal.

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Error using lsqcurvefit (line 271) Function value and YDATA sizes are not equal.

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