2.1.2 Predicting the Reliability of a Complex System Using an Artificial Neural Network

Abstract

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.

Adaptive Activity Driven Multi‐Level Hierarchical Prediction of Complex Systems Through Profiling and Feedback

“…They have been used for various purposes. For examples, Barr [Barr 2008] have used them for organization, learning and cooperation, Bode [Bode 1998 ] have used them for decision support in the management of research and development, Breitler [Breitler 2004] …”

“…They have been used for various purposes. For examples, Barr [Barr 2008] have used them for organization, learning and cooperation, Bode [Bode 1998 ] have used them for decision support in the management of research and development, Breitler [Breitler 2004] for predicting reliability of complex systems, On Cheung [On Cheung 2006] have used them for predicting project performance, Selby [Selby 2006] for prediction in large scale systems engineering while William Chien [Chien 1999] have used them for strategic planning. Finally, they have been intensively used in embedded systems (example [Chtourou 2006]).…”