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

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

Apr 1995

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1995spie.2492.1087p&link_type=abstract

Proc. SPIE Vol. 2492, p. 1087-1095, Applications and Science of Artificial Neural Networks, Steven K. Rogers; Dennis W. Ruck; Ed

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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 briefly present a new dynamic architecture able to solve the limitations of the previous architectures through an automatic redefinition of the topology. This architecture is applied to real time recognition of particle tracks in high energy accelerators and in astrophysics experiments.

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