Neural ordinary differential grey algorithm to forecasting MEVW systems

Because of the advantage of the gray theory for forecasting small sample time data, gray algorithm theory has definitely been extensively utilized since it has been proposed and is currently being widely developed for predicting frames particularly in small sample problems. This article presented a...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:International journal of computers, communications & control Jg. 19; H. 1
Hauptverfasser: Chen, Zy, Meng, Yahui, Wang, Ruei-Yuan, Chen, Timothy
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oradea Agora University of Oradea 01.02.2024
Schlagworte:
Algorithms
Differential equations
Forecasting
Mathematical models
Runge-Kutta method
Vehicle wheels
ISSN:1841-9836, 1841-9844
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Abstract Because of the advantage of the gray theory for forecasting small sample time data, gray algorithm theory has definitely been extensively utilized since it has been proposed and is currently being widely developed for predicting frames particularly in small sample problems. This article presented a viewpoint called gray algorithm by neuron- based ordinary-differential equation (NODE), called NODGM (neuron-based ordinary-differential gray-mode). In this type, we learn prediction methods through a training process that includes whiting equations. Compared with other models, the structure and time series via the regularity of real-samples are required in advance, so this NODGM design can have a better feasibility of applications and also study the origins of data according to different samples. The purpose is obtaining a better design with high forecast effectiveness, this study uses NODGM to train the model, while Runge-Kutta method is used to have the forecast set and solve numerical framwork. This algorithmic design creates a favorable theoretical basis for the installation of new process and distributes the numerical dimensions of completely mechanically elastic vehicle wheels (MEVW) in practical simulations.
AbstractList Because of the advantage of the gray theory for forecasting small sample time data, gray algorithm theory has definitely been extensively utilized since it has been proposed and is currently being widely developed for predicting frames particularly in small sample problems. This article presented a viewpoint called gray algorithm by neuron- based ordinary-differential equation (NODE), called NODGM (neuron-based ordinary-differential gray-mode). In this type, we learn prediction methods through a training process that includes whiting equations. Compared with other models, the structure and time series via the regularity of real-samples are required in advance, so this NODGM design can have a better feasibility of applications and also study the origins of data according to different samples. The purpose is obtaining a better design with high forecast effectiveness, this study uses NODGM to train the model, while Runge-Kutta method is used to have the forecast set and solve numerical framwork. This algorithmic design creates a favorable theoretical basis for the installation of new process and distributes the numerical dimensions of completely mechanically elastic vehicle wheels (MEVW) in practical simulations.
Author Chen, Timothy
Wang, Ruei-Yuan
Meng, Yahui
Chen, Zy
Author_xml – sequence: 1
  givenname: Zy
  surname: Chen
  fullname: Chen, Zy
– sequence: 2
  givenname: Yahui
  surname: Meng
  fullname: Meng, Yahui
– sequence: 3
  givenname: Ruei-Yuan
  surname: Wang
  fullname: Wang, Ruei-Yuan
– sequence: 4
  givenname: Timothy
  surname: Chen
  fullname: Chen, Timothy
BookMark eNp9kE1PAjEQhhuDiYj8AU-beN61X9uWoyGIJqgXP45N6U6xZNliWw78excwHjw4l5lM3mcmeS7RoAsdIHRNcEVqxeStX1trK4opq0jFhRRnaEgUJ-VEcT74nZm4QOOU1rgvRhWW9RDNn2EXTVuE2PjOxH3ReOcgQpd9v11F2BemXYXo8-emyKFwIYI1KftuVTzN3j-KtE8ZNukKnTvTJhj_9BF6u5-9Th_Kxcv8cXq3KC0RXJTAcVNzVjusMBjspDKScqOWgrJmiYWsG5CGSkqNw0vMGJPcEQkglFGUWzZCN6e72xi-dpCyXodd7PqXmk4I4wQzxfuUOqVsDClFcNr6bLIPXY7Gt5pgfTSnj-b0wZwm-mCuR-kfdBv9pjfzH_QN2bR0Dw
CitedBy_id crossref_primary_10_1017_S0890060424000325
ContentType Journal Article
Copyright 2024. This work is published under https://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml – notice: 2024. This work is published under https://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID AAYXX
CITATION
8FE
8FG
ABUWG
AFKRA
ARAPS
AZQEC
BENPR
BGLVJ
CCPQU
DWQXO
GNUQQ
HCIFZ
JQ2
K7-
P5Z
P62
PHGZM
PHGZT
PIMPY
PKEHL
PQEST
PQGLB
PQQKQ
PQUKI
DOI 10.15837/ijccc.2023.1.4676
DatabaseName CrossRef
ProQuest SciTech Collection
ProQuest Technology Collection
ProQuest Central (Alumni)
ProQuest Central UK/Ireland
Advanced Technologies & Computer Science Collection
ProQuest Central Essentials
ProQuest Central Database Suite (ProQuest)
ProQuest Technology Collection
ProQuest One Community College
ProQuest Central Korea
ProQuest Central Student
SciTech Collection (ProQuest)
ProQuest Computer Science Collection
Computer Science Database
ProQuest advanced technologies & aerospace journals
ProQuest Advanced Technologies & Aerospace Collection
Proquest Central Premium
ProQuest One Academic (New)
Publicly Available Content Database
ProQuest One Academic Middle East (New)
ProQuest One Academic Eastern Edition (DO NOT USE)
ProQuest One Applied & Life Sciences
ProQuest One Academic (retired)
ProQuest One Academic UKI Edition
DatabaseTitle CrossRef
Publicly Available Content Database
Advanced Technologies & Aerospace Collection
Computer Science Database
ProQuest Central Student
Technology Collection
ProQuest One Academic Middle East (New)
ProQuest Advanced Technologies & Aerospace Collection
ProQuest Central Essentials
ProQuest Computer Science Collection
ProQuest One Academic Eastern Edition
ProQuest Central (Alumni Edition)
SciTech Premium Collection
ProQuest One Community College
ProQuest Technology Collection
ProQuest SciTech Collection
ProQuest Central
Advanced Technologies & Aerospace Database
ProQuest One Applied & Life Sciences
ProQuest One Academic UKI Edition
ProQuest Central Korea
ProQuest Central (New)
ProQuest One Academic
ProQuest One Academic (New)
DatabaseTitleList CrossRef
Publicly Available Content Database
Database_xml – sequence: 1
  dbid: PIMPY
  name: ProQuest Publicly Available Content Database
  url: http://search.proquest.com/publiccontent
  sourceTypes: Aggregation Database
DeliveryMethod fulltext_linktorsrc
Discipline Computer Science
EISSN 1841-9844
ExternalDocumentID 10_15837_ijccc_2023_1_4676
GroupedDBID .4S
.DC
29J
2WC
5GY
AAKPC
AAYXX
ACIPV
ADBBV
AENEX
AFFHD
AFKRA
ALMA_UNASSIGNED_HOLDINGS
ARAPS
ARCSS
BCNDV
BENPR
BGLVJ
CCPQU
CITATION
E3Z
EDO
EOJEC
GROUPED_DOAJ
HCIFZ
ITG
ITH
K7-
MK~
ML~
M~E
OBODZ
OK1
OVT
PHGZM
PHGZT
PIMPY
PQGLB
TR2
TUS
8FE
8FG
ABUWG
AZQEC
DWQXO
GNUQQ
JQ2
P62
PKEHL
PQEST
PQQKQ
PQUKI
ID FETCH-LOGICAL-c1646-e40d5435f080ea0f78a724a8b623db0675de7a2722af0b033374f17ee68a824c3
IEDL.DBID P5Z
ISSN 1841-9836
IngestDate Fri Jul 25 23:47:47 EDT 2025
Sat Nov 29 03:05:06 EST 2025
Tue Nov 18 21:18:56 EST 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 1
Language English
License http://creativecommons.org/licenses/by-nc/4.0
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c1646-e40d5435f080ea0f78a724a8b623db0675de7a2722af0b033374f17ee68a824c3
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
OpenAccessLink https://www.proquest.com/docview/2913410384?pq-origsite=%requestingapplication%
PQID 2913410384
PQPubID 5045567
ParticipantIDs proquest_journals_2913410384
crossref_citationtrail_10_15837_ijccc_2023_1_4676
crossref_primary_10_15837_ijccc_2023_1_4676
PublicationCentury 2000
PublicationDate 20240201
PublicationDateYYYYMMDD 2024-02-01
PublicationDate_xml – month: 02
  year: 2024
  text: 20240201
  day: 01
PublicationDecade 2020
PublicationPlace Oradea
PublicationPlace_xml – name: Oradea
PublicationTitle International journal of computers, communications & control
PublicationYear 2024
Publisher Agora University of Oradea
Publisher_xml – name: Agora University of Oradea
SSID ssj0000328075
ssib032305687
Score 2.2897055
Snippet Because of the advantage of the gray theory for forecasting small sample time data, gray algorithm theory has definitely been extensively utilized since it has...
SourceID proquest
crossref
SourceType Aggregation Database
Enrichment Source
Index Database
SubjectTerms Algorithms
Differential equations
Forecasting
Mathematical models
Runge-Kutta method
Vehicle wheels
Title Neural ordinary differential grey algorithm to forecasting MEVW systems
URI https://www.proquest.com/docview/2913410384
Volume 19
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVAON
  databaseName: DOAJ Directory of Open Access Journals
  customDbUrl:
  eissn: 1841-9844
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000328075
  issn: 1841-9836
  databaseCode: DOA
  dateStart: 20060101
  isFulltext: true
  titleUrlDefault: https://www.doaj.org/
  providerName: Directory of Open Access Journals
– providerCode: PRVHPJ
  databaseName: ROAD: Directory of Open Access Scholarly Resources
  customDbUrl:
  eissn: 1841-9844
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000328075
  issn: 1841-9836
  databaseCode: M~E
  dateStart: 20060101
  isFulltext: true
  titleUrlDefault: https://road.issn.org
  providerName: ISSN International Centre
– providerCode: PRVPQU
  databaseName: Advanced Technologies & Aerospace Database
  customDbUrl:
  eissn: 1841-9844
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000328075
  issn: 1841-9836
  databaseCode: P5Z
  dateStart: 20060301
  isFulltext: true
  titleUrlDefault: https://search.proquest.com/hightechjournals
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: Computer Science Database
  customDbUrl:
  eissn: 1841-9844
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000328075
  issn: 1841-9836
  databaseCode: K7-
  dateStart: 20060301
  isFulltext: true
  titleUrlDefault: http://search.proquest.com/compscijour
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: ProQuest Central Database Suite (ProQuest)
  customDbUrl:
  eissn: 1841-9844
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000328075
  issn: 1841-9836
  databaseCode: BENPR
  dateStart: 20060301
  isFulltext: true
  titleUrlDefault: https://www.proquest.com/central
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: ProQuest Publicly Available Content Database
  customDbUrl:
  eissn: 1841-9844
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000328075
  issn: 1841-9836
  databaseCode: PIMPY
  dateStart: 20060301
  isFulltext: true
  titleUrlDefault: http://search.proquest.com/publiccontent
  providerName: ProQuest
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV05T8MwFLa4BhbKKc7KAxsyJI5bOxMCVA5BqwhxL5HjOBwqLTQFiYXfznuuA2JhYfFi55Cf_fw9P_v7CNlsqkA3eRYz3eAZExYwnLKBZHlg4wI8pcqc1uHVmex01M1NnPgNt9Ifq6x8onPUed_gHvkOxxQxsnmL3ZdXhqpRmF31EhrjZBJZElC6IWncVeMp4oiPPfxwnjninnsX4pqQxQozl-4eTQPitJ3HJ2OQ1ZBH2-E2OJDm77Xqt6t2689h7b9_PktmPPKke6OhMkfGbG-e1CpVB-on-QI5Qr4OaAhRqburSysNFfAFXQrh-QfV3Xv4wPDhmQ77FFCvNbrE49O03bq6piNy6HKRXB62Lg6OmZdbYAZJxpgVQd4A9FQAiLQ6KKTSkgutMkBIeYaRRW6l5pJzXQTQu1EkRRFKa5tKKy5MtEQmev2eXSZURzEAgTAP8OJqXpg4LgqljZCRQDqZbIWEVcemxnORoyRGN8WYBI2ROmOkaIw0TNEYK2Tr-5mXERPHn63XK2OkflaW6Y8lVv-uXiPT8C4xOp29TiaGgze7QabM-_CxHNTJ5H6rk5zXXfwO5alkULY_W3U3_KA-OWknt1_wv9zU
linkProvider ProQuest
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LT9wwEB7xkuAC9IF4tfWhPSFDYnvXzqFCiPLSLisOQLm5juPw0LILZKHiT_EbmcmjiAs3Dj3HsZJ845lvYs83AN_bJnJtkSbctUTKVUAOZ0KkeRaFJEdPadKy1-FpV_d65uwsORqDp6YWho5VNj6xdNTZ0NM_8g1BW8Sk5q02b245dY2i3dWmhUZlFp3w-BdTtuLnwS_E94cQuzvH2_u87irAPWlp8aCirIUkIUeuFFyUa-O0UM6kSASylAh0FrQTWgiXR2kkpdQqj3UIbeOMUF7ivOMwqaTRtK46mjf2KwXx8ZrulJFAilrrF_OomCeGdkrLup0W5oUbl1fek4qikOvxOjqs9uvY-Do0lPFud-5_-1LzMFsza7ZVLYUPMBYGH2Gu6VrBaif2CfZIjwQH4oOVtcis6RGDvq7PztG0meuf4wuNLq7ZaMiQ1QfvCjoezg53Tn-zSvy6-Awn7_I6CzAxGA7CIjAnEyQ6cRZRYW6W-yTJc-O80lKRXE66BHEDpPW11jq1_OhbyrkIfFuCbwl8G1sCfwnW_t1zUymNvDl6tQHf1l6nsC_IL799-RtM7x8fdm33oNdZgRmcV1Un0VdhYnR3H77AlH8YXRZ3X0sDZ_Dnve3kGc7mMOA
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Neural+ordinary+differential+grey+algorithm+to+forecasting+MEVW+systems&rft.jtitle=International+journal+of+computers%2C+communications+%26+control&rft.au=Chen%2C+Zy&rft.au=Meng%2C+Yahui&rft.au=Wang%2C+Ruei-Yuan&rft.au=Chen%2C+Timothy&rft.date=2024-02-01&rft.pub=Agora+University+of+Oradea&rft.issn=1841-9836&rft.eissn=1841-9844&rft.volume=19&rft.issue=1&rft_id=info:doi/10.15837%2Fijccc.2023.1.4676
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1841-9836&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1841-9836&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1841-9836&client=summon