Strategy of Data Selection for Adaptive Automation

Dale F. Rudd
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Dale F. Rudd: The University of Wisconsin, Madison, Wisconsin

Operations Research, 1962, vol. 10, issue 2, 232-248

Abstract: The problems of selection of logged process variables by a control computer with limited memory for continuous on-line process automation in the presence of erroneous, erratic and irrelevant data sources are considered from a strategic viewpoint. Markov and simple learning strategies are presented that allow a computer to learn about the current behavior of a process with respect to variable relevancy, resulting in a considerable increase in data selection efficiency over a random selection strategy. The case of single variable selection using a Markov strategy is solved analytically and computer simulation is used to estimate the performance of a simple learning strategy.

Date: 1962
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Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:10:y:1962:i:2:p:232-248

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