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Estimating sample sizes and statistical filtering of on-line measurements

Two important features which affect the efficiency of an on-line optimization strategy are noise and the time spent on measuring the optimization criterion, or let it be called sample size. The time spent on measuring the optimization criterion, i.e. the sample size, may slow down the control strate...

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Detalles Bibliográficos
Autor principal: Daneels, A
Lenguaje:eng
Publicado: 1972
Materias:
Acceso en línea:http://cds.cern.ch/record/875612
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author Daneels, A
author_facet Daneels, A
author_sort Daneels, A
collection CERN
description Two important features which affect the efficiency of an on-line optimization strategy are noise and the time spent on measuring the optimization criterion, or let it be called sample size. The time spent on measuring the optimization criterion, i.e. the sample size, may slow down the control strategy. Consequently the sample size will result from a compromise and be small enough not to slow down the strategy unduly, and large enough to estimate the true value of the sample with confidence. In this paper a simple statistical technique is suggested to solve both problems. Nevertheless, classical filtering techniques adapted to computers do exist; in this paper a second order filter will be considered for comparison. However, the statistical solution may be favoured when implementing it in an on-line control strategy: it follows closely the current stability of the process, has almost no time delay and is more akin to an algorithm requiring very little computation. (4 refs).
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institution Organización Europea para la Investigación Nuclear
language eng
publishDate 1972
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spelling cern-8756122019-09-30T06:29:59Zhttp://cds.cern.ch/record/875612engDaneels, AEstimating sample sizes and statistical filtering of on-line measurementsDetectors and Experimental TechniquesTwo important features which affect the efficiency of an on-line optimization strategy are noise and the time spent on measuring the optimization criterion, or let it be called sample size. The time spent on measuring the optimization criterion, i.e. the sample size, may slow down the control strategy. Consequently the sample size will result from a compromise and be small enough not to slow down the strategy unduly, and large enough to estimate the true value of the sample with confidence. In this paper a simple statistical technique is suggested to solve both problems. Nevertheless, classical filtering techniques adapted to computers do exist; in this paper a second order filter will be considered for comparison. However, the statistical solution may be favoured when implementing it in an on-line control strategy: it follows closely the current stability of the process, has almost no time delay and is more akin to an algorithm requiring very little computation. (4 refs).oai:cds.cern.ch:8756121972
spellingShingle Detectors and Experimental Techniques
Daneels, A
Estimating sample sizes and statistical filtering of on-line measurements
title Estimating sample sizes and statistical filtering of on-line measurements
title_full Estimating sample sizes and statistical filtering of on-line measurements
title_fullStr Estimating sample sizes and statistical filtering of on-line measurements
title_full_unstemmed Estimating sample sizes and statistical filtering of on-line measurements
title_short Estimating sample sizes and statistical filtering of on-line measurements
title_sort estimating sample sizes and statistical filtering of on-line measurements
topic Detectors and Experimental Techniques
url http://cds.cern.ch/record/875612
work_keys_str_mv AT daneelsa estimatingsamplesizesandstatisticalfilteringofonlinemeasurements