Construction of a Predictive Model of a Nonstationary Random Process and Analysis of Its Trends and Covariance Functions.
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| Title: | Construction of a Predictive Model of a Nonstationary Random Process and Analysis of Its Trends and Covariance Functions. |
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| Authors: | Stroganov, V. Yu.1 (AUTHOR), Belashova, I. S.2 (AUTHOR) Irina455@inbox.ru |
| Source: | Journal of Experimental & Theoretical Physics. Mar2026, Vol. 142 Issue 3, p234-239. 6p. |
| Subjects: | Simulation methods & models, Queueing networks, Distribution (Probability theory), Prediction models, Mathematical optimization, Stochastic processes |
| Abstract: | This article proposes an approach to solving optimization problems where adequate estimates of the functional under study can only be obtained using simulation models of the systems under consideration. Such models include queueing network models with complex topological structures and probabilistic service time formalizations with general distribution laws. In these cases, analytical models cannot provide adequate calculation results for various model parameterizations. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Experimental & Theoretical Physics is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194452481 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Construction of a Predictive Model of a Nonstationary Random Process and Analysis of Its Trends and Covariance Functions. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Stroganov%2C+V%2E+Yu%2E%22">Stroganov, V. Yu.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Belashova%2C+I%2E+S%2E%22">Belashova, I. S.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> Irina455@inbox.ru</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Experimental+%26+Theoretical+Physics%22">Journal of Experimental & Theoretical Physics</searchLink>. Mar2026, Vol. 142 Issue 3, p234-239. 6p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Simulation+methods+%26+models%22">Simulation methods & models</searchLink><br /><searchLink fieldCode="DE" term="%22Queueing+networks%22">Queueing networks</searchLink><br /><searchLink fieldCode="DE" term="%22Distribution+%28Probability+theory%29%22">Distribution (Probability theory)</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Stochastic+processes%22">Stochastic processes</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This article proposes an approach to solving optimization problems where adequate estimates of the functional under study can only be obtained using simulation models of the systems under consideration. Such models include queueing network models with complex topological structures and probabilistic service time formalizations with general distribution laws. In these cases, analytical models cannot provide adequate calculation results for various model parameterizations. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Experimental & Theoretical Physics is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=194452481 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1134/S106377612660087X Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 6 StartPage: 234 Subjects: – SubjectFull: Simulation methods & models Type: general – SubjectFull: Queueing networks Type: general – SubjectFull: Distribution (Probability theory) Type: general – SubjectFull: Prediction models Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Stochastic processes Type: general Titles: – TitleFull: Construction of a Predictive Model of a Nonstationary Random Process and Analysis of Its Trends and Covariance Functions. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Stroganov, V. Yu. – PersonEntity: Name: NameFull: Belashova, I. S. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 10637761 Numbering: – Type: volume Value: 142 – Type: issue Value: 3 Titles: – TitleFull: Journal of Experimental & Theoretical Physics Type: main |
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