Informatics and Applications
2016, Volume 10, Issue 2, pp 58-64
RECOGNITION OF DEPENDENCES ON THE BASIS OF INVERSE MAPPING
- A. N. Tyrsin
- S. M. Serebryanskii
Abstract
The article describes the method of recognition of dependences based on the use of inverse mapping. From a given finite set of models, one chooses the model that best fits the sample data. For each model, the selective dependence corresponding to it is determined by the sample. For the one-dimensional case, each selective dependence is mapped to the same reference model in the form of the straight line equation by means of inverse mapping. For each model, sample data are mapped to the same equation of the straight line with some mistakes. It is suggested to use the minimum of variance of mistakes as the criterion of adequacy of the constructed model of sample of data. In the case of multidimensional dependences, a heuristic method is suggested according to which a set of inverse functions for each of explanatory variables is considered for each model. Approbation of the method by means of statistical modeling by the Monte-Carlo method is carried out.
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[+] About this article
Title
RECOGNITION OF DEPENDENCES ON THE BASIS OF INVERSE MAPPING
Journal
Informatics and Applications
2016, Volume 10, Issue 2, pp 58-64
Cover Date
2016-05-30
DOI
10.14357/19922264160206
Print ISSN
1992-2264
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
recognition; functional dependence; model; inverse function; sample; variance; approximation
Authors
A. N. Tyrsin  and S. M. Serebryanskii 
Author Affiliations
Science and Engineering Center "Reliability and Resource of Large Systems and Machines," Ural Branch of the Russian Academy of Sciences; 54a Studencheskaya Str., Yekaterinburg 620049, Russian Federation
Troitsk Branch of Chelyabinsk State University, 9 S. Rasin Str., Troitsk 457100, Russian Federation
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