Optimization of Neural Network Identification of a Non-Stationary Object Based On Spline Functions

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dc.creatorIbragimovich, Jumanov Isroil
dc.creatorAbdusalyamovich, Djuraev Botir
dc.date2022-02-16
dc.date.accessioned2023-08-21T07:42:07Z
dc.date.available2023-08-21T07:42:07Z
dc.descriptionA technique for smoothing a dynamic process based on basis-spline functions and calculating information recovery coefficients has been developed, which helps to optimize the training of a neural network data processing system by reducing the errors of the training subset. Methods and algorithms for modeling the processes of smoothing, processing, and restoring data of non-stationary processes based on cubic spline functions are studied.en-US
dc.formatapplication/pdf
dc.identifierhttps://openaccessjournals.eu/index.php/ijiaet/article/view/1021
dc.identifier.urihttp://dspace.umsida.ac.id/handle/123456789/13833
dc.languageeng
dc.publisherOpen Access Journalsen-US
dc.relationhttps://openaccessjournals.eu/index.php/ijiaet/article/view/1021/970
dc.rightsCopyright (c) 2022 International Journal of Innovative Analyses and Emerging Technologyen-US
dc.sourceInternational Journal of Innovative Analyses and Emerging Technology; Vol. 2 No. 2 (2022): International Journal of Innovative Analyses and Emerging Technology (2792-4025); 49-55en-US
dc.source2792-4025
dc.subjectidentificationen-US
dc.subjectnon-stationary objecten-US
dc.subjectspline functionen-US
dc.subjectneural networken-US
dc.subjectoptimizationen-US
dc.subjectrecognitionen-US
dc.subjectforecastingen-US
dc.titleOptimization of Neural Network Identification of a Non-Stationary Object Based On Spline Functionsen-US
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typePeer-reviewed Articleen-US
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