The effect of internal parameters and geometry on the performance of back-propagation neural networks: an empirical study
Artificial neural networks of the back-propagation type are being used increasingly for modelling environmental systems. One of the most difficult, and least understood, tasks in the design of back-propagation networks is the choice of adequate internal network parameters and appropriate network geometries. Although some guidance is available for the choice of these values, they are generally determined using a trial and error approach. This paper describes the effect of geometry and internal parameters on network performance for a particular case study. Although the information obtained from the tests carried out in this research is specific to the problem considered, it provides users of back-propagation networks with a valuable guide on the behaviour of networks under a wide range of operating conditions. The results obtained indicate that learning rate, momentum, the gain of the transfer function, epoch size and network geometry have a significant impact on training speed, but not on generalisation ability. The type of transfer and error function used was found to have a significant impact on learning speed as well as generalisation ability.
internal parameters geometry artificial neural networks generalisation ability learning speed back-propagation algorithm network design