how to minimizing the error and find the constants by fitting the data
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I have two data sets, D1 and D2. where D1 and D2 has the experimental and Calculated values. How to fit the data to minimize the error and find the constant values.
D1=[......] %experiemntal
D2=[......] % calculated
X =[......] % dimensions are same as D1&D2
Y =[......] % dimensions are same as D1&D2
D2(A, B, X,Y)= A*exp(B/X)*Y % D2 is a function of A,B,X,Y
How to find the values of A and B by minimizing the error between D1 and D2?
Thanks
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dpb
2015 年 2 月 25 日
編集済み: dpb
2015 年 2 月 26 日
Presuming have the Stat Toolbox, ninfit makes it pretty simple directly...
If you write
function dhat=model(coef,x)
% some convenient internal shorthand definitions...
a=coef(1);
b=coef(2);
X=x(:,1);
Y=x(:,2);
% predicted model
dhat=a*exp(b./x).*y;
then
coeff=nlinfit([X,Y],D1,@model,coeff0);
where coeff0 is an initial guess/estimate for the coefficients a and b
Alternatively, the above form can be linearized by log transformation into
ln(A) + B/X + ln(Y)
which can be solved by OLS for coefficients in transformed space.
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