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A Machine Learning Algorithm to Estimate Minimal Cut and Path Sets from a Monte Carlo Simulation

  • C. M. Rocco
  • M. Muselli

Abstract

In this paper a novel approach based on a machine learning algorithm (Hamming Clustering) is proposed to estimate the minimal cut and path sets, using the samples generated by a Monte Carlo simulation and any Evaluation Function. Two examples show the potential of the proposed approach.

Keywords

Monte Carlo Boolean Function Binary String Minimal Path Fault Tree Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London 2004

Authors and Affiliations

  • C. M. Rocco
    • 1
  • M. Muselli
    • 2
  1. 1.Universidad CentralCaracasVenezuela
  2. 2.Istituto di Elettronica e di Ingegneria dell’Informazione e delle TelecomunicazioniConsiglio Nazionale delle RicercheGenovaItaly

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