How to improve precision of lsqnonlin analysis

I used lsqnonlin annlysis to fit a model to two sets of data. The results showed that the differences between original data and fitted line are rather huge (see in Figure1). How to improve this analysis to obtain better results? Thanks! Below are my codes.
function Result = myfunc1(a,p,G,m,fraction)
totalconcentration = 0.0000254;
n = 2;
K = exp(-((G + m .* fraction)./(8.314*298)));
opts = optimoptions(‘fsolve’,’Algorithm’, ‘levenberg-marquardt’,’FunctionTolerance’,1.0000e-12);
monomerconcentration = fsolve(@denaturationfun,0.999,opts);
function y = denaturationfun(x)
y = K.*totalconcentration – a.^(-1).*(((a.*x).^(n+1)).*(n.*a.*x-n-1)./((a.*x-1).^2)+a.*x./((a.*x-1).^2))+a.^(n-1).*((x^(n+1)).*(n.*x-n-1)./((x-1).^2));
end
Result = p * (1- (monomerconcentration ./ ( K * totalconcentration )));
end

clc;
clear;
close all;
fractiondata = [0.0431 0.0478 0.0525 0.0571 0.0617 0.0662 0.0707 0.0751 0.0795 0.0839 0.0882 0.0925 0.0967 0.1009 0.1051 0.1092 0.1133 0.1174 0.1253];
DegreeofAggdata = [0.86089 0.90051 0.84268 0.9543 0.98855 0.98538 1 0.98493 0.91339 0.92209 0.85817 0.78529 0.64172 0.45712 0.24855 0.11291 0.00812 0 0.0169];
fun = @(x)DegreeofAggdata – myfunc1(x(1),x(2),x(3),x(4),fractiondata);
x0 = [0.001,1,-50000,100000];
lb = [0,0.7,-150000,20000];
ub = [1,1.3,-5000,150000];
options = optimoptions(‘lsqnonlin’,’Algorithm’,’levenberg-marquardt’);
R = lsqnonlin(fun,x0,lb,ub); %% fit model to exp data
A = 0.03:0.001:0.15;
D = zeros(length(A),1);
for i = 1:length(A)
y = myfunc1(R(1),R(2),R(3),R(4),A(i));
D(i)= y;
end
figure(1);
plot(A,D,’r-‘); %% draw lines with fitted parameters
hold on;
plot (fractiondata,DegreeofAggdata,’-go’);I used lsqnonlin annlysis to fit a model to two sets of data. The results showed that the differences between original data and fitted line are rather huge (see in Figure1). How to improve this analysis to obtain better results? Thanks! Below are my codes.
function Result = myfunc1(a,p,G,m,fraction)
totalconcentration = 0.0000254;
n = 2;
K = exp(-((G + m .* fraction)./(8.314*298)));
opts = optimoptions(‘fsolve’,’Algorithm’, ‘levenberg-marquardt’,’FunctionTolerance’,1.0000e-12);
monomerconcentration = fsolve(@denaturationfun,0.999,opts);
function y = denaturationfun(x)
y = K.*totalconcentration – a.^(-1).*(((a.*x).^(n+1)).*(n.*a.*x-n-1)./((a.*x-1).^2)+a.*x./((a.*x-1).^2))+a.^(n-1).*((x^(n+1)).*(n.*x-n-1)./((x-1).^2));
end
Result = p * (1- (monomerconcentration ./ ( K * totalconcentration )));
end

clc;
clear;
close all;
fractiondata = [0.0431 0.0478 0.0525 0.0571 0.0617 0.0662 0.0707 0.0751 0.0795 0.0839 0.0882 0.0925 0.0967 0.1009 0.1051 0.1092 0.1133 0.1174 0.1253];
DegreeofAggdata = [0.86089 0.90051 0.84268 0.9543 0.98855 0.98538 1 0.98493 0.91339 0.92209 0.85817 0.78529 0.64172 0.45712 0.24855 0.11291 0.00812 0 0.0169];
fun = @(x)DegreeofAggdata – myfunc1(x(1),x(2),x(3),x(4),fractiondata);
x0 = [0.001,1,-50000,100000];
lb = [0,0.7,-150000,20000];
ub = [1,1.3,-5000,150000];
options = optimoptions(‘lsqnonlin’,’Algorithm’,’levenberg-marquardt’);
R = lsqnonlin(fun,x0,lb,ub); %% fit model to exp data
A = 0.03:0.001:0.15;
D = zeros(length(A),1);
for i = 1:length(A)
y = myfunc1(R(1),R(2),R(3),R(4),A(i));
D(i)= y;
end
figure(1);
plot(A,D,’r-‘); %% draw lines with fitted parameters
hold on;
plot (fractiondata,DegreeofAggdata,’-go’); I used lsqnonlin annlysis to fit a model to two sets of data. The results showed that the differences between original data and fitted line are rather huge (see in Figure1). How to improve this analysis to obtain better results? Thanks! Below are my codes.
function Result = myfunc1(a,p,G,m,fraction)
totalconcentration = 0.0000254;
n = 2;
K = exp(-((G + m .* fraction)./(8.314*298)));
opts = optimoptions(‘fsolve’,’Algorithm’, ‘levenberg-marquardt’,’FunctionTolerance’,1.0000e-12);
monomerconcentration = fsolve(@denaturationfun,0.999,opts);
function y = denaturationfun(x)
y = K.*totalconcentration – a.^(-1).*(((a.*x).^(n+1)).*(n.*a.*x-n-1)./((a.*x-1).^2)+a.*x./((a.*x-1).^2))+a.^(n-1).*((x^(n+1)).*(n.*x-n-1)./((x-1).^2));
end
Result = p * (1- (monomerconcentration ./ ( K * totalconcentration )));
end

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