nonlinear regression for multiple variable parameter
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I'm trying to write code to perform a nonlinear regression with multiple variables, but i have no idea how to do it.
i need to use Keq and pressure like Xdata and define the x1,x2,x3 from the regression.
I hope I have explained myself.
Function value and YDATA sizes are not equal.
Error in prova3 (line 49)
x_fit = lsqcurvefit(T, x0, [pressure Keq], temperature);
clc
clear all
close all
%%
%read all .xlsx file inside the folder
path = dir('C:\Users\Andrea\Desktop\matlab\input_file\*.xlsx');
%cicle for obtain the sample data (P-T-Ni) content directly from the .xlsx file
for i=1:1
table = readtable(strcat('C:\Users\Andrea\Desktop\matlab\input_file\',path(i).name));
temperature(:,i) = table.temperature;
pressure(:,i) = table.pressure;
Ni_grt(:,i) = table.Ni_Grt;
Ni_ol(:,i) = table.Ni_Ol;
Cr_grt(:,i) = table.Wt_Cr;
Ca_grt(:,i) = table.Wt_Ca;
end
%% calculation
%Keq
Ni_grt = Ni_grt(~isnan(Ni_grt), :);
Keq= log(Ni_grt/2900);
%temperature from C to K
temperature = temperature + 273.15;
%olivine mean
Ni_ol = Ni_ol(~isnan(Ni_ol), :);
mean_Ni_ol = mean(Ni_ol);
%% ignore the NaN value
temperature = temperature(~isnan(temperature), :);
pressure = pressure(~isnan(pressure), :);
Ca_grt = Ca_grt(~isnan(Ca_grt), :);
Cr_grt = Cr_grt(~isnan(Cr_grt), :);
%% Calculate the parameters (ΔH, ΔV, ΔS)
% function of T
T = @(x,pressure,Keq) (x(1) + pressure * x(2)) ./ (x(3) - log(Keq));
% random start parameters (ΔH, ΔV, ΔS)
x0 = [1, 1, 1];
% nonlinear regression with lsqcurvefit
x_fit = lsqcurvefit(T, x0, [pressure Keq], temperature);
% Extract the fitted parameters
DeltaH = x_fit(1);
DeltaV = x_fit(2);
DeltaS = x_fit(3);
% the new value od temperature from Ni_grt of database
predicted_temperature = T(x_fit, pressure);
% print the result
fprintf('Parametri adattati:\n');
fprintf('ΔH = %.2f\n', DeltaH);
fprintf('ΔV = %.2f\n', DeltaV);
fprintf('ΔS = %.2f\n', DeltaS);
fprintf('Ni_ol = %.2f\n', mean_Ni_ol);
%% diff TNi-T
deltaT = predicted_temperature-temperature;
mean_deltaT = mean(deltaT);
fprintf('mean_detaT = %.2f\n', mean_deltaT);
采纳的回答
Your code works. The only change required was to create the ‘pressure_Keq’ matrix as the independent variable and then change ‘T’ to refer to it correctly internally.
Try this —
clc
clear all
close all
%%
%read all .xlsx file inside the folder
% path = dir('C:\Users\Andrea\Desktop\matlab\input_file\*.xlsx');
path = dir('*.xlsx');
%cicle for obtain the sample data (P-T-Ni) content directly from the .xlsx file
for i=1:1
% table = readtable(strcat('C:\Users\Andrea\Desktop\matlab\input_file\',path(i).name));
table = readtable(path(i).name)
temperature(:,i) = table.temperature;
pressure(:,i) = table.pressure;
Ni_grt(:,i) = table.Ni_Grt;
Ni_ol(:,i) = table.Ni_Ol;
Cr_grt(:,i) = table.Wt_Cr;
Ca_grt(:,i) = table.Wt_Ca;
end
%% calculation
%Keq
Ni_grt = Ni_grt(~isnan(Ni_grt), :);
Keq= log(Ni_grt/2900);
%temperature from C to K
temperature = temperature + 273.15;
%olivine mean
Ni_ol = Ni_ol(~isnan(Ni_ol), :);
mean_Ni_ol = mean(Ni_ol);
%% ignore the NaN value
temperature = temperature(~isnan(temperature), :);
pressure = pressure(~isnan(pressure), :);
Ca_grt = Ca_grt(~isnan(Ca_grt), :);
Cr_grt = Cr_grt(~isnan(Cr_grt), :);
%% Calculate the parameters (ΔH, ΔV, ΔS)
% function of T
pressure_Keq = [pressure Keq]; % Create The Independent Variable As A Matrix, Then Address Its Columns Appropriately In The Objective Function
T = @(x,pressure_Keq) (x(1) + pressure_Keq(:,1) * x(2)) ./ (x(3) - log(pressure_Keq(:,2)));
% random start parameters (ΔH, ΔV, ΔS)
x0 = [1, 1, 1];
% nonlinear regression with lsqcurvefit
x_fit = lsqcurvefit(T, x0, pressure_Keq, temperature);
% Extract the fitted parameters
DeltaH = x_fit(1);
DeltaV = x_fit(2);
DeltaS = x_fit(3);
% the new value od temperature from Ni_grt of database
predicted_temperature = T(x_fit, pressure_Keq);
% print the result
fprintf('Parametri adattati:\n');
fprintf('ΔH = %.2f\n', DeltaH);
fprintf('ΔV = %.2f\n', DeltaV);
fprintf('ΔS = %.2f\n', DeltaS);
fprintf('Ni_ol = %.2f\n', mean_Ni_ol);
%% diff TNi-T
deltaT = predicted_temperature-temperature;
mean_deltaT = mean(deltaT);
fprintf('mean_detaT = %.2f\n', mean_deltaT);
.
2 个评论
andrea rigotto
2023-9-28
编辑：andrea rigotto
2023-9-28
Star Strider
2023-9-28
That may be because you are calculating the logarithm of a negative number.
I just noticed this now, that these two calculations appear to be redundant:
Keq = log(Ni_grt/2900);
that division is likely to result in values less than 1 (so the log of ‘Keq’ will be negative in those instances), and later you take the log of ‘Keq’ in ‘T’:
T = @(x,pressure_Keq) (x(1) + pressure_Keq(:,1) * x(2)) ./ (x(3) - log(pressure_Keq(:,2)));
Perhaps taking the log of ‘Keq’ in ‘T’ needs to be changed, so that you are only subtracting ‘Keq’ and not the log of it.
I do not know what you are calculating, so I am hesitant to make any other changes to your code.
.
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