Neural nets with varying topology for high energy particle recognition: theory and applications

摘要

In this paper we start from a critical analysis of the fundamental problems of the parallel calculus in linear structures and of their extension to the partial solutions obtained with non-linear architectures. Then, we present shortly a new dynamic architecture able to solve the limitations of the previous archietctures through an automatic re-definition of the topology. This architecture is applied to real time recognition of particle tracks in high energy accelerators and in astrophysics experiments.