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2.1.2 Predicting the Reliability of a Complex System Using an Artificial Neural Network
Author(s) -
Breitler Alan L.,
Sloan Crystal D.
Publication year - 2004
Publication title -
incose international symposium
Language(s) - English
Resource type - Journals
ISSN - 2334-5837
DOI - 10.1002/j.2334-5837.2004.tb00491.x
Subject(s) - artificial neural network , reliability (semiconductor) , computer science , reliability engineering , complex system , artificial intelligence , machine learning , engineering , physics , quantum mechanics , power (physics)
The capability to predict the reliability of complex systems that must be deployed without overly prolonged or expensive testing is of increasing importance to the military test and evaluation community. The presentation of subsystem reliability data to an artificial neural network is a critical factor in the capability of such networks to produce accurate system predictions. By producing a matrix of values corresponding to subsystem reliabilities, using a zero (0) for a nonexistent parallel resource and a one (1) for a nonexistent series subsystem, it was possible to train an artificial neural network to accurately predict the overall system reliability.