Stochastic identification of the “Object-attribute” table based on the modified Rabiner’s method

This article is about solving the problem of stochastic identification of the “Object-attribute” table based on subsets of stochastic ergodic matrices. The table has N rows and m columns. The identification is based on the implementation of the modified Rabiner’s method. We assume that the elements...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of physics. Conference series Jg. 1925; H. 1; S. 12014 - 12020
Hauptverfasser: Shalagin, S, Nurutdinova, A
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bristol IOP Publishing 01.05.2021
Schlagworte:
ISSN:1742-6588, 1742-6596
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:This article is about solving the problem of stochastic identification of the “Object-attribute” table based on subsets of stochastic ergodic matrices. The table has N rows and m columns. The identification is based on the implementation of the modified Rabiner’s method. We assume that the elements of m columns of the table are a discrete Markov chain of length N . The identification of each column is based on calculating the maximum probability that the Markov chain is generated based on the distribution law represented by one ergodic stochastic matrix from a given subset. An algorithm for solving this problem is proposed. Estimates of the time and hardware complexity of this algorithm, which are executed in parallel on a distributed computing system, are obtained. The dependence of the obtained estimates on the number of rows and columns of the identified table is determined. The value of N has a linear effect on the time complexity of an algorithm that implements MMR and is executed in parallel. A promising direction for future research is the distributed implementation of the proposed Algorithm.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1925/1/012014