Stabilizing machine learning prediction of dynamics: Novel noise-inspired regularization tested with reservoir computing.

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Title: Stabilizing machine learning prediction of dynamics: Novel noise-inspired regularization tested with reservoir computing.
Authors: Wikner, Alexander1, awikner1@umd.edu, Harvey, Joseph1,2, Girvan, Michelle1, Hunt, Brian R.3, Pomerance, Andrew4, Antonsen, Thomas1,5, Ott, Edward1,5
Source: Neural Networks; Feb2024, Vol. 170, p94-110, 17p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 174842682
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Stabilizing machine learning prediction of dynamics: Novel noise-inspired regularization tested with reservoir computing.
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  Data: <searchLink fieldCode="JN" term="%22Neural+Networks%22">Neural Networks</searchLink>; Feb2024, Vol. 170, p94-110, 17p
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=174842682
RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1016/j.neunet.2023.10.054
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 17
        StartPage: 94
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      – TitleFull: Stabilizing machine learning prediction of dynamics: Novel noise-inspired regularization tested with reservoir computing.
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            NameFull: Wikner, Alexander
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            NameFull: Harvey, Joseph
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            NameFull: Girvan, Michelle
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            NameFull: Pomerance, Andrew
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            NameFull: Antonsen, Thomas
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            – D: 01
              M: 02
              Text: Feb2024
              Type: published
              Y: 2024
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              Value: 170
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