  The effect of the sample radius on the total photoacoustic signal processed
by neural networks trained with undistorded and distorded signals is carefully
analized for modulation frequencies from 20 to 20 kHz. This is done for signals
generated for a 400micrometers thick Si n-type plate, whose radius varies from
2 to 7 mm. It is found that the networks trained with both undistorded or
distorded signals yield the best predictions for sample radii between 2 and 3
mm, which is close to the used microphone aperture radius of 1.5 mm. The
network trained only with undistorted signals gives the best results for sample
radii comparable to the microphone dimensions. The obtained results justify the
validity of a theoretical model derived for a sample radius equals to the
microphone aperture and indicate the experimental necessity to use samples with
radii comparable to this aperture.
