Gradient scanning with fmincon

I am using fmincon to curve fitting by minimizing residual sum of s开发者_如何转开发quares. When I do not have very many data points, fmincon usually finds local minima that are not close the the global minima which will lead to a good fit. Is there a way to use a gradient scanning method with fmincon to avoid these local minima?

Since your problem has local minima, these simple optimization procedures will result in different answers for different initial condition. Try initializing with a reasonable guess, or initialize with multiple random values and choose the one with smallest error.

We can help you better, if you fully describe your problem (What your curve is, and possibly a code snippet.)