BEHAVIOR OF FEEDFORWARD NEURAL NETWORKS IN INVARIANT TRACK FINDING

We performed several simulations with feed-forward neural networks usi ng an idealized tracking apparatus with tracks invariant under transla tion and roto-translation transformations. Input information was provi ded to the networks without any preprocessing. We implemented 2 and 3 layer architectures up to 50000 connections, and we tested the influen ce of parameters such as learning rate, momentum, number of learning f iles and noise rejection on the classification efficiency. The general ization ability-is not so good as expected, whereas the classification efficiency is larger than 90% for almost all the architectures, the i nfluence of the above mentioned parameters being less than 10% overall except for the noise rejection for which it increases up to 20%.