Abstract: Deciding the numerous parameters involved in
designing a competent artificial neural network is a complicated task.
The existence of several options for selecting an appropriate
architecture for neural network adds to this complexity, especially
when different applications of heterogeneous natures are concerned.
Two completely different applications in engineering and medical
science were selected in the present study including prediction of
workpiece's surface roughness in ultrasonic-vibration assisted turning
and papilloma viruses oncogenicity. Several neural network
architectures with different parameters were developed for each
application and the results were compared. It was illustrated in this
paper that some applications such as the first one mentioned above
are apt to be modeled by a single network with sufficient accuracy,
whereas others such as the second application can be best modeled
by different expert networks for different ranges of output.
Development of knowledge about the essentials of neural networks
for different applications is regarded as the cornerstone of
multidisciplinary network design programs to be developed as a
means of reducing inconsistencies and the burden of the user
intervention.