Paper Title:
Reliability Inference for Products of Weibull Distribution by Using Artificial Neural Network Formal Description
  Abstract

Back-propagation network (BPN) has the advantage of simulating a nonlinear system that is difficult to describe by a physical model. This study introduces a back-propagation network methodology to estimate the accelerated life reliability. The environmental stresses and failure times are chosen as the input variables. An optimum prediction system is acquired by adjusting the number of neurons in the hidden layer and the output layer of neural networks. For a numerical example, the developed BPN architecture is applied to real accelerated life testing data of the STNLCD modules which are distributed as a Weibull distribution. By the research result, we can have the conclusion that the BPN methodology is practical to make the reliability inference with the advantages of self-learning ability even without mathematics models.

  Info
Periodical
Key Engineering Materials (Volumes 326-328)
Edited by
Soon-Bok Lee and Yun-Jae Kim
Pages
573-576
DOI
10.4028/www.scientific.net/KEM.326-328.573
Citation
Y. C. Chen, P. H. Lee, C. M. Chen, "Reliability Inference for Products of Weibull Distribution by Using Artificial Neural Network Formal Description", Key Engineering Materials, Vols. 326-328, pp. 573-576, 2006
Online since
December 2006
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Price
$32.00
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