Representing diverse mathematical problems using neural networks in hybrid intelligent systems.
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| Title: | Representing diverse mathematical problems using neural networks in hybrid intelligent systems. |
|---|---|
| Authors: | Xing Li, Hong, Li, L.X |
| Source: | Expert Systems. Nov99, Vol. 16 Issue 4, p262. 11p. 18 Diagrams. |
| Subjects: | Artificial neural networks, Expert systems, Linear programming, Mathematical models |
| Abstract: | In recent years, artificial neural networks have attracted considerable attention as candidates for novel computational systems. Computer scientists and engineers are developing neural networks as representational and computational models for problem solving: neural networks are expected to produce new solutions or alternatives to existing models. This paper demonstrates the flexibility of neural networks for modeling and solving diverse mathematical problems including Taylor series expansion, Weierstrass's first approximation theorem, linear programming with single and multiple objectives, and fuzzy mathematical programming. Neural network representations of such mathematical problems may make it possible to overcome existing limitations, to find new solutions or alternatives to existing models, and to achieve synergistic effects through hybridization. [ABSTRACT FROM AUTHOR] |
| Copyright of Expert Systems is the property of Wiley-Blackwell 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 | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 4370471 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Representing diverse mathematical problems using neural networks in hybrid intelligent systems. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Xing+Li%2C+Hong%22">Xing Li, Hong</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+L%2EX%22">Li, L.X</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Expert+Systems%22">Expert Systems</searchLink>. Nov99, Vol. 16 Issue 4, p262. 11p. 18 Diagrams. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Expert+systems%22">Expert systems</searchLink><br /><searchLink fieldCode="DE" term="%22Linear+programming%22">Linear programming</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+models%22">Mathematical models</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In recent years, artificial neural networks have attracted considerable attention as candidates for novel computational systems. Computer scientists and engineers are developing neural networks as representational and computational models for problem solving: neural networks are expected to produce new solutions or alternatives to existing models. This paper demonstrates the flexibility of neural networks for modeling and solving diverse mathematical problems including Taylor series expansion, Weierstrass's first approximation theorem, linear programming with single and multiple objectives, and fuzzy mathematical programming. Neural network representations of such mathematical problems may make it possible to overcome existing limitations, to find new solutions or alternatives to existing models, and to achieve synergistic effects through hybridization. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Expert Systems is the property of Wiley-Blackwell 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=4370471 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1111/1468-0394.00118 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 262 Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Expert systems Type: general – SubjectFull: Linear programming Type: general – SubjectFull: Mathematical models Type: general Titles: – TitleFull: Representing diverse mathematical problems using neural networks in hybrid intelligent systems. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Xing Li, Hong – PersonEntity: Name: NameFull: Li, L.X IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov99 Type: published Y: 1999 Identifiers: – Type: issn-print Value: 02664720 Numbering: – Type: volume Value: 16 – Type: issue Value: 4 Titles: – TitleFull: Expert Systems Type: main |
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