Discrete Event Dynamic Systems and Perturbation Analysis

扰动分析（PA），更具体地， 无限小PA（IPA）已被开发为 对真实的世界问题的反应。 简单IPA计算 使用保持时间序列的导数估计 在确定性相似性假设下， 轨迹序列的状态部分是不变的， 扰动 这总是违反了一个限制， 无限长的采样路径或无限大的 有限样本路径的集合。 研究的目的是调查以下主题： (1)IPA分析的通用剪切和粘贴方法， (2)具有偏置校正和平滑PA的IPA， (3)A段方法和减少差异， (4)微调真实的软件性能 使用PA分析计算。

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.