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- W1336364 abstract "Regression models play an essential role in the description of industrial processes in petroleum-processing and petroleum-chemical plants. However, the wide range of measuring hardware introduced in industrial plants in recent years has raised the number of observed parameters to many tens under normal operating conditions and even to hundreds in set-up/tune-up conditions. Therefore, designers of process control systems find it necessary to restrict the number of variables used in the mathematical model. In this article, the authors estimate the efficiency of the selection of the best subset of variables (SBSV) and their corresponding algorithms. The authors use a computer to implement the SBSV algorithm. The SBSV algorithm is designed to select an optimal variable subset of pre-specified dimension. It combines enumeration with the calculation of the residual sum of squares. The algorithm is free from rounding error build-up, its speed is comparable to current algorithm systems, and it requires roughly half as much memory." @default.
- W1336364 created "2016-06-24" @default.
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- W1336364 date "1986-09-01" @default.
- W1336364 modified "2023-09-23" @default.
- W1336364 title "Efficiency analysis of an algorithm selecting the best subset of variables in a regression model" @default.
- W1336364 hasPublicationYear "1986" @default.
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