On Evaluating SFM-Based Infinitesimal Perturbation Analysis Estimates From Discrete Event System Data

This paper investigates the evaluation of infinitesimal perturbation analysis (IPA) estimates that have been derived based on a stochastic fluid model (SFM) using data observed from the sample path of a discrete event system (DES). First, we show that a straightforward implementation of the SFM-base...

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Bibliographic Details
Published in:IEEE transactions on automatic control 2008-03, Vol.53 (2), p.560-565
Main Authors: Panayiotou, C.G., Markou, M.M.
Format: Article
Language:English
Subjects:
Applied sciences
Approximation
Communication system control
Computational fluid dynamics
Computer science
control theory
systems
Computer systems and distributed systems. User interface
Control theory. Systems
Counting circuits
Data analysis
Derivatives
Discrete event systems
Discrete event systems (DESs)
Estimates
Exact sciences and technology
Fluid flow
Fluids
Modelling and identification
Optimization methods
Performance analysis
perturbation analysis
Perturbation methods
Quality management
Software
stochastic fluid models (SFMs)
Stochastic processes
Stochastic systems
Studies
Yield estimation
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Summary:This paper investigates the evaluation of infinitesimal perturbation analysis (IPA) estimates that have been derived based on a stochastic fluid model (SFM) using data observed from the sample path of a discrete event system (DES). First, we show that a straightforward implementation of the SFM-based IPA estimates may yield biased estimates when the data are obtained from the actual DES. Then, in order to better approximate the sample path of the DES, we propose a special case of SFM where the arrival and service processes are modeled by piecewise constant on/off sources. The proposed SFM violates some of the assumptions made in [1]-[4] , and, as a result, the sample derivatives no longer exist. However, using the proposed SFM, we obtain the left and right sided sample derivative estimates. As shown in this paper, the sided sample derivatives are much better in approximating the required derivatives compared to the straightforward implementation of the SFM-based IPA estimates.
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2007.914259