Fit of the function y(x) = A[1-exp(-kx)] to data strongly embedded in noise

An algorithm which allows the fitting of the function g(x) = A [1-exp(-kx)] to data points (xsub(i),gsub(i)), i = 0, ..., N is presented. The method is based on the introduction of an extra parameter acting as catalyst. This algorithm is specially suited to data strongly embedded in noise. A comparison with the conventional linearization method has been made. (orig.)