sense of two term exponential model fitting

Hi. I have data in which Y axis decrease as the X is increasing. Now I want to fit it exponentially.

Firstly I tried single term exponential model. I get result as f(x) = a*exp(b*x)

     Coefficients (with 95% confidence bounds):
       a =       44.56  (42.77, 46.36)
       b =     -0.4552  (-0.4744, -0.4361)

here -0.4552 is my exponential slope, namely rate of decrease. However I am not satisfied with the figure due to bad fitting. Then I tried two-term exponential fitting, and i get:

f(x) = a*exp(b*x) + c*exp(d*x)

     Coefficients (with 95% confidence bounds):
       a =       71.51  (68.16, 74.86)
       b =       -0.66  (-0.6801, -0.64)
       c =  -8.108e+07  (-2.139e+08, 5.17e+07)
       d =      -9.655  (-10.63, -8.68)

In matlabs help it is said that if b and d both negative then the data tends to decrease. So nothing else.

In my case I have total 5 plots. for all plots the b and d values are as follows:

   -0.6600   -9.6555
   -0.5496  -15.4602
   -5.3828   -0.7509
   -1.3416    2.6037
   -5.5994   -0.5741
I couldnt figure out how to compare the plots. which plot more stable?. I actually should know how to use b and d.

Any suggestion (including math link) is appreciated.

[Image Analyst]

Show a plot of what your data look like. And, what function did you use to do the fitting? And was it the Curve Fitting Toolbox or the Statistics and Machine Learning Toolbox (please list it below in the products section).