This repo contains a tool for fitting functions by using Diferential
Evolution(DE) to select parameters of a predefined function F that
minimize the MSE(Mean Square Error) between the F and
experimental/known/given points.
	The tool was tested on GNU Octave, but it should work fine on MATLAB.
	Keep in mind that the algorithm is randomic and don't always converge
to the global minimum, run it as much times as possible/viable/ before
using the choosen parameters.

==========================================================================

	Example: First you need to define the function to be fitted, here is an
simple example(concentration of a reagent in a reactor over time):

		function c = concentration(t, fixedParams, params) c0 = fixedParams(1);
		res = params(1); k = params(2); c = res+(c0-res).*exp(-k.*t); end

	This function receives a column vector t and returns a c row vector.  The
params input is 'parsed' and its values are used to compute c.  The
fixedParams input is also 'parsed', but the DE is not going to change them
in order to get better MSE results.
	Now we call the fit function:
		[params, err] = fit(t, measuredC, [0 0], [500, 1], [measuredC(1)] ...
				@concentration);
	The inputs [0 0] and [500 1] are the lowest and highest acceptable values
for the fitted/tunned parameters, respectivelly.