How to use fminsearch to minimize sum of the squares and find the constant values?

I have two data sets, D1 and D2. where D1 and D2 has the experimental and Calculated values. How to find the constant values by minimizing the sum of the squares(sum(D1-D2)^2 ==0).

Sum (s)= (D1-D2)^2 ==0

D1=[......] %experiemntal
D2=[......] % calculated
X =[......] % dimensions are same as D1&D2
Y =[......] % dimensions are same as D1&D2

D2= a*exp(b/X)*Y %%D2 is a function of A,B,X,Y

Sum (s)= (D1-D2)^2 ==0 %% the difference in the sum of the squres between D1 and D2 should be close to zero.

How to find the values of a and b by minimizing the sum of the squares between D1 and D2?

Thanks

[Matt J]

You accepted an answer to an identical question several weeks ago

http://www.mathworks.com/matlabcentral/answers/180348-how-to-minimizing-the-error-and-find-the-constants-by-fitting-the-data

So, it is not clear why you would be re-launching the question.

[dpb]

There's an example under doc fminsearch of the subject "Curve Fitting via Optimization" one of which is, in fact, an exponential.

OTOH, if you happen to have the Optimization Toolbox, lsqnonlin saves writing the explicit residual.

[R7 DR]

Hi

That answer is good and working fine.

But, I read in the internet that we can use 'fminsearch' also to solve these type of problems. My function is very senstive to initial guess, so I want try by using 'fminsearch' function. Actually I want to check which one is giving the better results for my function.

Thanks