Periodical
A Two-Timescale Duplex Neurodynamic Approach to Mixed-Integer Optimization.
| Τίτλος: | A Two-Timescale Duplex Neurodynamic Approach to Mixed-Integer Optimization. |
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| Συγγραφείς: | Che, Hangjun, Wang, Jun |
| Πηγή: | IEEE Transactions on Neural Networks & Learning Systems; Jan2021, Vol. 32 Issue 1, p36-48, 13p |
| Θεματικοί όροι: | Recurrent neural networks, Particle swarm optimization, Benchmark problems (Computer science), Biological neural networks, Mixed integer linear programming |
| Περίληψη: | This article presents a two-timescale duplex neurodynamic approach to mixed-integer optimization, based on a biconvex optimization problem reformulation with additional bilinear equality or inequality constraints. The proposed approach employs two recurrent neural networks operating concurrently at two timescales. In addition, particle swarm optimization is used to update the initial neuronal states iteratively to escape from local minima toward better initial states. In spite of its minimal system complexity, the approach is proven to be almost surely convergent to optimal solutions. Its superior performance is substantiated via solving five benchmark problems. [ABSTRACT FROM AUTHOR] |
| Copyright of IEEE Transactions on Neural Networks & Learning Systems is the property of IEEE 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.) | |
| Βάση Δεδομένων: | Biomedical Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edm&genre=article&issn=2162237X&ISBN=&volume=32&issue=1&date=20210101&spage=36&pages=36-48&title=IEEE Transactions on Neural Networks & Learning Systems&atitle=A%20Two-Timescale%20Duplex%20Neurodynamic%20Approach%20to%20Mixed-Integer%20Optimization.&aulast=Che%2C%20Hangjun&id=DOI:10.1109/TNNLS.2020.2973760 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Items | – Name: Title Label: Title Group: Ti Data: A Two-Timescale Duplex Neurodynamic Approach to Mixed-Integer Optimization. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Che%2C+Hangjun%22">Che, Hangjun</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Jun%22">Wang, Jun</searchLink> – Name: TitleSource Label: Source Group: Src Data: IEEE Transactions on Neural Networks & Learning Systems; Jan2021, Vol. 32 Issue 1, p36-48, 13p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Recurrent+neural+networks%22">Recurrent neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Particle+swarm+optimization%22">Particle swarm optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Benchmark+problems+%28Computer+science%29%22">Benchmark problems (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22Biological+neural+networks%22">Biological neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Mixed+integer+linear+programming%22">Mixed integer linear programming</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This article presents a two-timescale duplex neurodynamic approach to mixed-integer optimization, based on a biconvex optimization problem reformulation with additional bilinear equality or inequality constraints. The proposed approach employs two recurrent neural networks operating concurrently at two timescales. In addition, particle swarm optimization is used to update the initial neuronal states iteratively to escape from local minima toward better initial states. In spite of its minimal system complexity, the approach is proven to be almost surely convergent to optimal solutions. Its superior performance is substantiated via solving five benchmark problems. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of IEEE Transactions on Neural Networks & Learning Systems is the property of IEEE 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: 10.1109/TNNLS.2020.2973760 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 36 Subjects: – SubjectFull: Recurrent neural networks Type: general – SubjectFull: Particle swarm optimization Type: general – SubjectFull: Benchmark problems (Computer science) Type: general – SubjectFull: Biological neural networks Type: general – SubjectFull: Mixed integer linear programming Type: general Titles: – TitleFull: A Two-Timescale Duplex Neurodynamic Approach to Mixed-Integer Optimization. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Che, Hangjun – PersonEntity: Name: NameFull: Wang, Jun IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 2162237X Numbering: – Type: volume Value: 32 – Type: issue Value: 1 Titles: – TitleFull: IEEE Transactions on Neural Networks & Learning Systems Type: main |
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