Parameter identification for nonlinear time-varying dynamic system based on the assumption of “short time linearly varying” and global constraint optimization
• The error of the traditional assumption of “short time invariant” was analyzed. • The assumption of “short time linearly varying” with better accuracy was proposed. • A global constraint optimization was introduced to improve the method's robustness. • The proposed identification method was v...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Chen, Tengfei [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Species loss from land use of oil palm plantations in Thailand - Jaroenkietkajorn, Ukrit ELSEVIER, 2021, mssp, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:139 ; year:2020 ; pages:0 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.ymssp.2020.106620 |
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ELV049496379 |
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| 520 | |a • The error of the traditional assumption of “short time invariant” was analyzed. • The assumption of “short time linearly varying” with better accuracy was proposed. • A global constraint optimization was introduced to improve the method's robustness. • The proposed identification method was verified through an SDOF numerical example. • A qualitative analysis on the identification window size was carried out. | ||
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Parameter identification for nonlinear time-varying dynamic system based on the assumption of “short time linearly varying” and global constraint optimization |
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• The error of the traditional assumption of “short time invariant” was analyzed. • The assumption of “short time linearly varying” with better accuracy was proposed. • A global constraint optimization was introduced to improve the method's robustness. • The proposed identification method was verified through an SDOF numerical example. • A qualitative analysis on the identification window size was carried out. |
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• The error of the traditional assumption of “short time invariant” was analyzed. • The assumption of “short time linearly varying” with better accuracy was proposed. • A global constraint optimization was introduced to improve the method's robustness. • The proposed identification method was verified through an SDOF numerical example. • A qualitative analysis on the identification window size was carried out. |
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• The error of the traditional assumption of “short time invariant” was analyzed. • The assumption of “short time linearly varying” with better accuracy was proposed. • A global constraint optimization was introduced to improve the method's robustness. • The proposed identification method was verified through an SDOF numerical example. • A qualitative analysis on the identification window size was carried out. |
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code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">• The error of the traditional assumption of “short time invariant” was analyzed. • The assumption of “short time linearly varying” with better accuracy was proposed. • A global constraint optimization was introduced to improve the method's robustness. • The proposed identification method was verified through an SDOF numerical example. • A qualitative analysis on the identification window 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