Akaike's information criterion is a measure of the goodness of fit of an estimated statistical model that accounts for both the fit quality and model complexity. One way to calculate AIC is as follows:

$\mathit{AIC}=2k + n[\ln(\mathit{RSS})]\,$

, where $k$ is the number of parameters in the model and $\mathit{RSS}$ is the residual sum of squares.

Assume two models:

$f_1(x) = ax$

and

$f_2(x) = \frac{ax}{b + cx} + d \sin(x)$

. Is the number of parameters, $k$, in both the models equal 1?

**EDIT** In the equations above only $x$ is a variable, while $a$, $b$, $c$ ... are constant parameters