ONLINE DETERMINATION OF STEADY-STATE IN SIMULATION OUTPUTS

As simulation output is generally nonstationary and autocorrelated and includes the initialization bias we can't directly apply traditional statistic approaches to its analysis. One of solutions for this proble m is to exclude the initial part of simulation output that is affected by this bias. Detection of steady stale is one of the most important issues for the automation of simulation output analysis. This paper de als with on-line detection of truncation point in order to estimate ef ficiently the steady-state mean of single-run simulation by batch mean s method. Two algorithms are purposed The first algorithm is based on the Euclidean distance equation and the second utilizes the backpropag ation algorithm in neural networks that has been applied to the patter n classification problems. (C) 1997 Elsevier Science Ltd.