Towards implementation of residual-feedback GMDH neural network on parallel GPU memory guided by a regression curve.
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| Title: | Towards implementation of residual-feedback GMDH neural network on parallel GPU memory guided by a regression curve. |
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| Authors: | Brito, Ricardo1, Fong, Simon1 ccfong@umac.mo, Cho, Kyungeun2, Song, Wei3, Wong, Raymond4, Mohammed, Sabah5, Fiaidhi, Jinan5 |
| Source: | Journal of Supercomputing. Oct2016, Vol. 72 Issue 10, p3993-4020. 28p. |
| Subjects: | GMDH algorithms, Artificial neural networks, Computer networks, Neural computers, Graphics processing units |
| Abstract: | GMDH, which stands for Group Method Data Handling, is an evolutionary type of neural network. It has received much attention in the supercomputing research community because of its ability to optimize its internal structure for maximum prediction accuracy. GMDH works by evolving itself from a basic network, expanding its number of neurons and hidden layer until no further performance gain can be obtained. Earlier on, the authors proposed a novel strategy that extends existing GMDH neural network techniques. The new strategy, called residual-feedback, retains and reuses past prediction errors as part of the multivariate sample data that provides relevant multivariate inputs to the GMDH neural networks. This is important because the strength of GMDH, like any neural network, is in predicting outcomes from multivariate data, and it is very noise-tolerant. GMDH is a well-known ensemble type of prediction method that is capable of modeling highly non-linear relations. Maximum accuracy is often achieved by using only the minimum amount of network neurons and simplest layered structure. This paper contributes to the technical design of implementing GMDH on GPU memory where all the weight computations run on parallel GPU memory blocks. It is a first step towards developing complex neural network architecture on GPU with the capability of evolving and expanding its structure to minimally sufficient for obtaining the maximum prediction accuracy based on the given input data. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Supercomputing is the property of Springer Nature 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: 118732411 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Towards implementation of residual-feedback GMDH neural network on parallel GPU memory guided by a regression curve. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Brito%2C+Ricardo%22">Brito, Ricardo</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Fong%2C+Simon%22">Fong, Simon</searchLink><relatesTo>1</relatesTo><i> ccfong@umac.mo</i><br /><searchLink fieldCode="AR" term="%22Cho%2C+Kyungeun%22">Cho, Kyungeun</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Song%2C+Wei%22">Song, Wei</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Wong%2C+Raymond%22">Wong, Raymond</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Mohammed%2C+Sabah%22">Mohammed, Sabah</searchLink><relatesTo>5</relatesTo><br /><searchLink fieldCode="AR" term="%22Fiaidhi%2C+Jinan%22">Fiaidhi, Jinan</searchLink><relatesTo>5</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Supercomputing%22">Journal of Supercomputing</searchLink>. Oct2016, Vol. 72 Issue 10, p3993-4020. 28p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22GMDH+algorithms%22">GMDH algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+networks%22">Computer networks</searchLink><br /><searchLink fieldCode="DE" term="%22Neural+computers%22">Neural computers</searchLink><br /><searchLink fieldCode="DE" term="%22Graphics+processing+units%22">Graphics processing units</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: GMDH, which stands for Group Method Data Handling, is an evolutionary type of neural network. It has received much attention in the supercomputing research community because of its ability to optimize its internal structure for maximum prediction accuracy. GMDH works by evolving itself from a basic network, expanding its number of neurons and hidden layer until no further performance gain can be obtained. Earlier on, the authors proposed a novel strategy that extends existing GMDH neural network techniques. The new strategy, called residual-feedback, retains and reuses past prediction errors as part of the multivariate sample data that provides relevant multivariate inputs to the GMDH neural networks. This is important because the strength of GMDH, like any neural network, is in predicting outcomes from multivariate data, and it is very noise-tolerant. GMDH is a well-known ensemble type of prediction method that is capable of modeling highly non-linear relations. Maximum accuracy is often achieved by using only the minimum amount of network neurons and simplest layered structure. This paper contributes to the technical design of implementing GMDH on GPU memory where all the weight computations run on parallel GPU memory blocks. It is a first step towards developing complex neural network architecture on GPU with the capability of evolving and expanding its structure to minimally sufficient for obtaining the maximum prediction accuracy based on the given input data. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Supercomputing is the property of Springer Nature 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: 10.1007/s11227-016-1740-9 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 28 StartPage: 3993 Subjects: – SubjectFull: GMDH algorithms Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Computer networks Type: general – SubjectFull: Neural computers Type: general – SubjectFull: Graphics processing units Type: general Titles: – TitleFull: Towards implementation of residual-feedback GMDH neural network on parallel GPU memory guided by a regression curve. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Brito, Ricardo – PersonEntity: Name: NameFull: Fong, Simon – PersonEntity: Name: NameFull: Cho, Kyungeun – PersonEntity: Name: NameFull: Song, Wei – PersonEntity: Name: NameFull: Wong, Raymond – PersonEntity: Name: NameFull: Mohammed, Sabah – PersonEntity: Name: NameFull: Fiaidhi, Jinan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2016 Type: published Y: 2016 Identifiers: – Type: issn-print Value: 09208542 Numbering: – Type: volume Value: 72 – Type: issue Value: 10 Titles: – TitleFull: Journal of Supercomputing Type: main |
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