Development of data-based model-free representation of non-conservative dissipative systems
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| Title: | Development of data-based model-free representation of non-conservative dissipative systems |
|---|---|
| Authors: | Tasbihgoo, Farzad1 tasbihgo@usc.edu, Caffrey, John P.2 jcaffey@csupomona.edu, Masri, Sami F.1 masri@usc.edu |
| Source: | International Journal of Non-Linear Mechanics. Jan2007, Vol. 42 Issue 1, p99-117. 19p. |
| Subjects: | Artificial neural networks, Mechanics (Physics), Simulation methods & models, Electronic excitation |
| Abstract: | Abstract: A general procedure is presented for developing data-based, non-parametric models of non-linear multi-degree-of-freedom, non-conservative, dissipative systems. Two broad classes of methods are discussed: one relying on the representation of the system restoring forces in a polynomial-basis format, and the other using artificial neural networks to map the complex transformations relating the system state variables to the needed system outputs. A non-linear two-degree-of-freedom system is used to formulate the approach under discussion and to generate synthetic data for calibrating the efficiency of the two methods in capturing complex non-linear phenomena (such as dry friction, hysteresis, dead-space non-linearities, and polynomial-type non-linearities) that are widely encountered in the applied mechanics field. Subsequently, a reconfigurable test apparatus was used to generate experimental measurements from a physical non-linear “joint” involving two-dimensional motion (translation and rotation) and complicated interaction forces between the different motion axes, among its internal elements. Both the polynomial-basis approach and the neural network method were used to develop high-fidelity, non-parametric models of the physical test article. The ability of the identified models to accurately “generalize” the essential features of the non-linear system was verified by comparing the predictions of the models with experimental measurements from data sets corresponding to different excitations than those used for identification purposes. It is shown that the identification techniques under discussion can be useful tools for developing accurate simulation models of complex multi-dimensional non-linear systems under broadband excitation. [Copyright &y& Elsevier] |
| Copyright of International Journal of Non-Linear Mechanics is the property of Pergamon Press - An Imprint of Elsevier Science 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: 24787054 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Development of data-based model-free representation of non-conservative dissipative systems – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Tasbihgoo%2C+Farzad%22">Tasbihgoo, Farzad</searchLink><relatesTo>1</relatesTo><i> tasbihgo@usc.edu</i><br /><searchLink fieldCode="AR" term="%22Caffrey%2C+John+P%2E%22">Caffrey, John P.</searchLink><relatesTo>2</relatesTo><i> jcaffey@csupomona.edu</i><br /><searchLink fieldCode="AR" term="%22Masri%2C+Sami+F%2E%22">Masri, Sami F.</searchLink><relatesTo>1</relatesTo><i> masri@usc.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Non-Linear+Mechanics%22">International Journal of Non-Linear Mechanics</searchLink>. Jan2007, Vol. 42 Issue 1, p99-117. 19p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Mechanics+%28Physics%29%22">Mechanics (Physics)</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation+methods+%26+models%22">Simulation methods & models</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+excitation%22">Electronic excitation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Abstract: A general procedure is presented for developing data-based, non-parametric models of non-linear multi-degree-of-freedom, non-conservative, dissipative systems. Two broad classes of methods are discussed: one relying on the representation of the system restoring forces in a polynomial-basis format, and the other using artificial neural networks to map the complex transformations relating the system state variables to the needed system outputs. A non-linear two-degree-of-freedom system is used to formulate the approach under discussion and to generate synthetic data for calibrating the efficiency of the two methods in capturing complex non-linear phenomena (such as dry friction, hysteresis, dead-space non-linearities, and polynomial-type non-linearities) that are widely encountered in the applied mechanics field. Subsequently, a reconfigurable test apparatus was used to generate experimental measurements from a physical non-linear “joint” involving two-dimensional motion (translation and rotation) and complicated interaction forces between the different motion axes, among its internal elements. Both the polynomial-basis approach and the neural network method were used to develop high-fidelity, non-parametric models of the physical test article. The ability of the identified models to accurately “generalize” the essential features of the non-linear system was verified by comparing the predictions of the models with experimental measurements from data sets corresponding to different excitations than those used for identification purposes. It is shown that the identification techniques under discussion can be useful tools for developing accurate simulation models of complex multi-dimensional non-linear systems under broadband excitation. [Copyright &y& Elsevier] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Non-Linear Mechanics is the property of Pergamon Press - An Imprint of Elsevier Science 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.ijnonlinmec.2006.10.021 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 99 Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Mechanics (Physics) Type: general – SubjectFull: Simulation methods & models Type: general – SubjectFull: Electronic excitation Type: general Titles: – TitleFull: Development of data-based model-free representation of non-conservative dissipative systems Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Tasbihgoo, Farzad – PersonEntity: Name: NameFull: Caffrey, John P. – PersonEntity: Name: NameFull: Masri, Sami F. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2007 Type: published Y: 2007 Identifiers: – Type: issn-print Value: 00207462 Numbering: – Type: volume Value: 42 – Type: issue Value: 1 Titles: – TitleFull: International Journal of Non-Linear Mechanics Type: main |
| ResultId | 1 |
