Abstract
The Threshold Voting System (TVS) is a generalization of k-out-of-n systems. It is widely used in human organization systems, technical decision-making systems, fault-tolerant systems, mutual exclusion in distributed systems, and replicated databases. The TVS comprises of n units, each of which provides a binary decision (0 or I), or abstains from voting. The system output is I if the cumulative weight of all I-opting units is at least a pre-specijied fraction τ of the cumulative weight of all non-abstaining units. Otherwise, the system output is 0. In this study, an irttuitive Monte Carlo simulation (MCS) was first developed to estimate the TVS reliability value. Then a new Artificial Neural Network (called MCS-ANN) and a Response Surface Methodology (called MCS-RSM) with the Box-Behnken design (BBD) were created to find the approximated reliability Jirnction from the reliability estimated by MCS. The efectiveness of these two approaches were also compared using a benchmark TVS. © 2007 IEEE.