Locating Arrays: A New Experimental Design for Screening Complex Engineered Systems

A. Aldaco, C. Colbourn, V. Syrotiuk
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引用次数: 22

Abstract

The purpose of a screening experiment is to identify significant factors and interactions on a response for a system. Engineered systems are complex in part due to their size. To apply traditional experimental designs for screening in complex engineered systems requires either restricting the factors considered, which automatically restricts the interactions to those in the set, or restricting interest to main effects, which fails to consider any possible interactions. To address this problem we propose a locating array (LA) as a screening design. Locating arrays exhibit logarithmic growth in the number of factors because their focus is on identification rather than on measurement. This makes practical the consideration of an order of magnitude more factors in experimentation than traditional screening designs. We present preliminary results applying an LA for screening the response of TCP throughput in a simulation model of a mobile wireless network. The full-factorial design for this system is infeasible (over 1043 design points!) yet an LA has only 421 design points. We validate the significance of the identified factors and interactions independently using the statistical software JMP. Screening using locating arrays is viable and yields useful models.
定位阵列:一种筛选复杂工程系统的新实验设计
筛选实验的目的是确定系统响应的重要因素和相互作用。工程系统之所以复杂,部分原因在于它们的大小。要将传统的实验设计应用于复杂工程系统的筛选,要么需要限制所考虑的因素,这会自动将相互作用限制在集合中的因素,要么需要限制对主效应的兴趣,这就没有考虑任何可能的相互作用。为了解决这个问题,我们提出了一种定位阵列(LA)作为筛选设计。定位阵列的因素数量呈对数增长,因为它们的重点是识别而不是测量。这使得实验中考虑的因素比传统筛选设计多出一个数量级。在移动无线网络的仿真模型中,我们提出了应用LA筛选TCP吞吐量响应的初步结果。这个系统的全因子设计是不可行的(超过1043个设计点!)然而一个LA只有421个设计点。我们使用统计软件JMP独立验证所识别的因素和相互作用的显著性。使用定位阵列进行筛选是可行的,并产生有用的模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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