Neural nets with varying topology for high-energy particle recognition: an outlook of computational dynamics

Antonio Luigi Perrone; Roberto Messi; Enrico Pasqualucci; Gianfranco Basti

doi:10.1117/12.152532

2 September 1993 Neural nets with varying topology for high-energy particle recognition: an outlook of computational dynamics

Antonio Luigi Perrone, Roberto Messi, Enrico Pasqualucci, Gianfranco Basti

Author Affiliations +

Proceedings Volume 1965, Applications of Artificial Neural Networks IV; (1993) https://doi.org/10.1117/12.152532
Event: Optical Engineering and Photonics in Aerospace Sensing, 1993, Orlando, FL, United States

Abstract

With respect to Rosenblatt linear perceptron, a classical limitation theorem demonstrated by M. Minsky and S. Papert is discussed. This theorem, '$PSI_One-in-a-box', ultimately concern the intrinsic limitations of parallel calculations in pattern calculations in pattern recognition problems. We demonstrate a possible solution of this limitation problem by substituting the static definition of characteristic functions and of their domains in the 'geometrical' perceptron, with their dynamic definition. This dynamics consists in the mutual redefinition of the characteristic function and of its domain depending on the matching with the input. We show an application of this 'dynamic' perceptron scheme in particle tracks recognition in high energy physics. Actually, this algorithm is being used for real time automatic triggering of ADONE e⁺e^- storage ring (Frascati, Rome) to evaluate the neutron time-like electromagnetic form factor in the context of 'Fenice' collaboration by Italian Institute of Nuclear Physics (INFN).

Citation Download Citation

Antonio Luigi Perrone, Roberto Messi, Enrico Pasqualucci, and Gianfranco Basti "Neural nets with varying topology for high-energy particle recognition: an outlook of computational dynamics", Proc. SPIE 1965, Applications of Artificial Neural Networks IV, (2 September 1993); https://doi.org/10.1117/12.152532

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