A MCS Based Neural Network Approach to Extract Network Approximate Reliability Function

Abstract

Simulations have been applied extensively to solve complex problems in real-world. They provide reference results and support the decision candidates in quantitative attributes. This paper combines ANN with Monte Carlo Simulation (MCS) to provide a reference model of predicting reliability of a network. It suggests reduced BBD design to select the input training data and opens the black box of neural networks through constructing the limited space reliability function from ANN parameters. Besides, this paper applies a practical problem that considers both cost and reliability to evaluate the performance of the ANN based reliability function. © Springer-Verlag Berlin Heidelberg 2007.