Discrete Event Dynamic Systems and Perturbation Analysis

微扰分析(PA)和更具体地说无穷小微扰分析(IPA)是作为对现实世界问题的响应而发展起来的。Simple IPA在确定性相似性假设下使用保持时间序列计算导数估计，假设轨迹序列的状态部分在扰动下不变。在无限长的样本路径或无限大的有限样本路径集合的限制中，总是违反这一点。这项研究的目的是研究以下问题：(1)用于IPA分析的广义剪切和粘贴法，(2)带有偏差校正和平滑PA的IPA，(3)A段方法和方差减少，(4)使用PA分析对实时计算的软件性能进行微调。

Perturbation Analysis (PA) and more particularly Infinitesimal PA (IPA) has been developed as a response to real world problems. Simple IPA calculates a derivative estimate using the holding time sequence under the deterministic similarity assumption that the state part of the trajectory sequence is invariant under perturbation. This is always violated in the limit of an infinitely long sample path or an infinitely large ensemble of finite sample path. The objective of the research is to investigate the following topics: (1) Generalized cut and paste method for IPA analysis, (2) IPA with bias correction and smoothed PA, (3) A-segment methodology and variance reduction, (4) fine tune the software performance of real time computing using PA analysis.