ISSN 1991-2927

ACP № 4 (58) 2019

Modification of the Step-by-step Regression Method for Mathematical Models of the Object Behavior Prediction

Gulnara Rivalevna Kadyrova, Ulyanovsk State Technical University, Candidate of Engineering; graduated from the Faculty of Radioengineering of Ulyanovsk Polytechnic Institute; Associate Professor at the Department of Applied Mathematics and Informatics of Ulyanovsk State Technical University; an author of monographs, textbooks, and articles in the field of statistical modeling, software information systems. [e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it. ]G. Kadyrova

Modification of the Step-by-step Regression Method for Mathematical Models of the Object Behavior Prediction 000_8.pdf

The temperature regime significantly affects the durability of the computer. Ensuring stability of the computer functioning considers the stability of the heating temperature of the main elements that should not exceed the specified values. The article discusses issues related to the timely warning about a possible violation of the temperature regime stability. In order to diagnose stability, the multivariate statistical control methods are proposed to use. Evaluation of stability is carried out with the use of two criteria: stability of the temperature average level and their dispersion. Independent parameters can be controlled with the use of standard shewhart charts. The algorithms on the basis of Hotelling statistics (for assessing stability of the middle-level temperature measurement process) and generalized variance (for evaluation of the dispersion process stability) are used for correlated parameters. The efficiency of these algorithms can be enhanced through the analysis of non-random structures on the control charts, the use of the warning border, as well as application of modifications based on the cumulative amounts or moving averages weighted exponentially. The multivariate statistical technique of the computer temperature regime control including monitoring in the context of a well-developed process for a training sample in order to separate the parameters controlled for a group of independent and correlated ones, process analysis for assessment of control characteristics and continuous monitoring of the process with the construction of Hotelling charts and generalized dispersion with identifying possible violations of the process based on the presence of significant structures and the use of a warning border. This technique is illustrated by the example of five computer temperature regime parameters control.


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