Design of a Computational Heuristic to Solve the Nonlinear Liénard Differential Model

In this study, the design of a computational heuristic based on the nonlinear Liénard model is presented using the efficiency of artificial neural networks (ANNs) along with the hybridization procedures of global and local search approaches. The global search genetic algorithm (GA) and local search...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Computer modeling in engineering & sciences Ročník 136; číslo 1; s. 201
Hlavní autori: Li, Yan, Sabir, Zulqurnain, Ilhan, Esin, Asif, Muhammad, Gao, Wei, Baskonus, Haci
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Henderson Tech Science Press 01.01.2023
Predmet:
Artificial neural networks
Boundary conditions
Design
Genetic algorithms
Heuristic
Quadratic programming
Searching
ISSN:1526-1492, 1526-1506
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Abstract In this study, the design of a computational heuristic based on the nonlinear Liénard model is presented using the efficiency of artificial neural networks (ANNs) along with the hybridization procedures of global and local search approaches. The global search genetic algorithm (GA) and local search sequential quadratic programming scheme (SQPS) are implemented to solve the nonlinear Liénard model. An objective function using the differential model and boundary conditions is designed and optimized by the hybrid computing strength of the GA-SQPS. The motivation of the ANN procedures along with GA-SQPS comes to present reliable, feasible and precise frameworks to tackle stiff and highly nonlinear differential models. The designed procedures of ANNs along with GA-SQPS are applied for three highly nonlinear differential models. The achieved numerical outcomes on multiple trials using the designed procedures are compared to authenticate the correctness, viability and efficacy. Moreover, statistical performances based on different measures are also provided to check the reliability of the ANN along with GA-SQPS.
AbstractList In this study, the design of a computational heuristic based on the nonlinear Liénard model is presented using the efficiency of artificial neural networks (ANNs) along with the hybridization procedures of global and local search approaches. The global search genetic algorithm (GA) and local search sequential quadratic programming scheme (SQPS) are implemented to solve the nonlinear Liénard model. An objective function using the differential model and boundary conditions is designed and optimized by the hybrid computing strength of the GA-SQPS. The motivation of the ANN procedures along with GA-SQPS comes to present reliable, feasible and precise frameworks to tackle stiff and highly nonlinear differential models. The designed procedures of ANNs along with GA-SQPS are applied for three highly nonlinear differential models. The achieved numerical outcomes on multiple trials using the designed procedures are compared to authenticate the correctness, viability and efficacy. Moreover, statistical performances based on different measures are also provided to check the reliability of the ANN along with GA-SQPS.
Author Baskonus, Haci
Sabir, Zulqurnain
Ilhan, Esin
Asif, Muhammad
Li, Yan
Gao, Wei
Author_xml – sequence: 1
  givenname: Yan
  surname: Li
  fullname: Li, Yan
– sequence: 2
  givenname: Zulqurnain
  surname: Sabir
  fullname: Sabir, Zulqurnain
– sequence: 3
  givenname: Esin
  surname: Ilhan
  fullname: Ilhan, Esin
– sequence: 4
  givenname: Muhammad
  surname: Asif
  fullname: Asif, Muhammad
– sequence: 5
  givenname: Wei
  surname: Gao
  fullname: Gao, Wei
– sequence: 6
  givenname: Haci
  surname: Baskonus
  fullname: Baskonus, Haci
BookMark eNo1jkFOwzAUBS1UJNrCAdhZYp3ybSdOvEQpUKQCCyq2lWt_g6vULrHDnTgHFyMSsJq3maeZkUmIAQm5ZLAQXEJ5bQ6YFhy4WACvQJUnZMoqLgtWgZz871LxMzJLaQ8gpGjUlLwuMfm3QKOjmrbxcByyzj4G3dEVDr1P2RuaI32J3SfS_I70KYbOB9Q9Xfvvr6B7S5feOewxZD9qj9Fid05One4SXvxxTjZ3t5t2Vayf7x_am3VxVE0urMLaAau0Eo5p1zQ7a3SNvNyBE1Y3yFGKUhppnQCjmW2YNjWrjBwVKZmYk6vf22MfPwZMebuPQz_Gp63gAIzzWknxA_qDVrc
ContentType Journal Article
Copyright 2023. This work is licensed under https://creativecommons.org/licenses/by/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: 2023. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID 7SC
7TB
8FD
8FE
8FG
ABJCF
ABUWG
AFKRA
ARAPS
AZQEC
BENPR
BGLVJ
CCPQU
COVID
DWQXO
FR3
GNUQQ
HCIFZ
JQ2
K7-
KR7
L6V
L7M
L~C
L~D
M7S
P5Z
P62
PHGZM
PHGZT
PIMPY
PKEHL
PQEST
PQGLB
PQQKQ
PQUKI
PTHSS
DOI 10.32604/cmes.2023.025094
DatabaseName Computer and Information Systems Abstracts
Mechanical & Transportation Engineering Abstracts
Technology Research Database
ProQuest SciTech Collection
ProQuest Technology Collection
Materials Science & Engineering Collection
ProQuest Central (Alumni)
ProQuest Central UK/Ireland
Advanced Technologies & Computer Science Collection
ProQuest Central Essentials
ProQuest Central
ProQuest Technology Collection
ProQuest One Community College
Coronavirus Research Database
ProQuest Central Korea
Engineering Research Database
ProQuest Central Student
SciTech Collection (ProQuest)
ProQuest Computer Science Collection
Computer Science Database
Civil Engineering Abstracts
ProQuest Engineering Collection
Advanced Technologies Database with Aerospace
Computer and Information Systems Abstracts – Academic
Computer and Information Systems Abstracts Professional
Engineering Database
Advanced Technologies & Aerospace Database
ProQuest Advanced Technologies & Aerospace Collection
Proquest Central Premium
ProQuest One Academic (New)
ProQuest 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
Engineering Collection
DatabaseTitle Publicly Available Content Database
Computer Science Database
ProQuest Central Student
Technology Collection
Technology Research Database
Computer and Information Systems Abstracts – Academic
ProQuest One Academic Middle East (New)
Mechanical & Transportation Engineering Abstracts
ProQuest Advanced Technologies & Aerospace Collection
ProQuest Central Essentials
ProQuest Computer Science Collection
Computer and Information Systems Abstracts
ProQuest Central (Alumni Edition)
SciTech Premium Collection
ProQuest One Community College
ProQuest Central
ProQuest One Applied & Life Sciences
ProQuest Engineering Collection
ProQuest Central Korea
ProQuest Central (New)
Advanced Technologies Database with Aerospace
Engineering Collection
Advanced Technologies & Aerospace Collection
Civil Engineering Abstracts
Engineering Database
ProQuest One Academic Eastern Edition
Coronavirus Research Database
ProQuest Technology Collection
ProQuest SciTech Collection
Computer and Information Systems Abstracts Professional
Advanced Technologies & Aerospace Database
ProQuest One Academic UKI Edition
Materials Science & Engineering Collection
Engineering Research Database
ProQuest One Academic
ProQuest One Academic (New)
DatabaseTitleList 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 1526-1506
GroupedDBID -~X
7SC
7TB
8FD
8FE
8FG
AAFWJ
ABJCF
ABUWG
ACIWK
ADMLS
AFKRA
ALMA_UNASSIGNED_HOLDINGS
ARAPS
AZQEC
BENPR
BGLVJ
CCPQU
COVID
DWQXO
EBS
EJD
F5P
FR3
GNUQQ
HCIFZ
J9A
JQ2
K7-
KR7
L6V
L7M
L~C
L~D
M7S
OK1
P62
PHGZM
PHGZT
PIMPY
PKEHL
PQEST
PQGLB
PQQKQ
PQUKI
PTHSS
RTS
ID FETCH-LOGICAL-p98t-d9e7f015a93f1af88bdca7e24b0f3da8e2e6346c6df30ca1d81ac715c65a96613
IEDL.DBID BENPR
ISSN 1526-1492
IngestDate Sat Sep 06 07:31:05 EDT 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed false
IsScholarly true
Issue 1
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-p98t-d9e7f015a93f1af88bdca7e24b0f3da8e2e6346c6df30ca1d81ac715c65a96613
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
OpenAccessLink https://www.proquest.com/docview/3200122796?pq-origsite=%requestingapplication%
PQID 3200122796
PQPubID 2048798
ParticipantIDs proquest_journals_3200122796
PublicationCentury 2000
PublicationDate 20230101
PublicationDateYYYYMMDD 2023-01-01
PublicationDate_xml – month: 01
  year: 2023
  text: 20230101
  day: 01
PublicationDecade 2020
PublicationPlace Henderson
PublicationPlace_xml – name: Henderson
PublicationTitle Computer modeling in engineering & sciences
PublicationYear 2023
Publisher Tech Science Press
Publisher_xml – name: Tech Science Press
SSID ssj0036389
Score 2.373507
Snippet In this study, the design of a computational heuristic based on the nonlinear Liénard model is presented using the efficiency of artificial neural networks...
SourceID proquest
SourceType Aggregation Database
StartPage 201
SubjectTerms Artificial neural networks
Boundary conditions
Design
Genetic algorithms
Heuristic
Quadratic programming
Searching
Title Design of a Computational Heuristic to Solve the Nonlinear Liénard Differential Model
URI https://www.proquest.com/docview/3200122796
Volume 136
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVPQU
  databaseName: Advanced Technologies & Aerospace Database
  customDbUrl:
  eissn: 1526-1506
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0036389
  issn: 1526-1492
  databaseCode: P5Z
  dateStart: 20000101
  isFulltext: true
  titleUrlDefault: https://search.proquest.com/hightechjournals
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: Computer Science Database
  customDbUrl:
  eissn: 1526-1506
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0036389
  issn: 1526-1492
  databaseCode: K7-
  dateStart: 20000101
  isFulltext: true
  titleUrlDefault: http://search.proquest.com/compscijour
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: Engineering Database
  customDbUrl:
  eissn: 1526-1506
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0036389
  issn: 1526-1492
  databaseCode: M7S
  dateStart: 20000101
  isFulltext: true
  titleUrlDefault: http://search.proquest.com
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: ProQuest Central Database Suite (ProQuest)
  customDbUrl:
  eissn: 1526-1506
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0036389
  issn: 1526-1492
  databaseCode: BENPR
  dateStart: 20000101
  isFulltext: true
  titleUrlDefault: https://www.proquest.com/central
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: ProQuest Publicly Available Content Database
  customDbUrl:
  eissn: 1526-1506
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0036389
  issn: 1526-1492
  databaseCode: PIMPY
  dateStart: 20000101
  isFulltext: true
  titleUrlDefault: http://search.proquest.com/publiccontent
  providerName: ProQuest
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LTwIxEG4EPHgRn_GBpAevFXZbut2TUYFgVLIRYtALabvdhARZhIX_5O_wjzndLTHx4MXLXiabNJ3pzDePzIfQJVNaANCNCZeQmzAmPSIgShCueBJTj6kmK8gmgn5fjEZh5ApuSzdWufGJuaOOU21r5A3q512gIOTX8w9iWaNsd9VRaJRQxW4qAzuv3Hb60fPGF1Mbj_ONqT4nkAv4RV8TIEuTNfS7sfu6fXplcYBlLf7ljfMQ063-93B7aNeBS3xTWMM-2jKzA1TdEDdg944P0Us7n9vAaYIlLsSuJoh7ZlXsbsZZigfpdG0wQETcLzZqyAV-nHx9zsCscNtRq4CLmGJLqTY9QsNuZ3jXI45ggcxDkZE4NEECcECGNPFkIoSKtQyMDwpKaCyF8Q2njGseJ7SppRcLT-rAa2kOv0Bcp8eoPEtn5gRh1VKAswAMmUAy3jKCSSmaSijIFo1K9CmqbS5u7B7Jcvxza2d_i8_RjtVTUfmooXK2WJkLtK3X2WS5qDud11HpISB1O7w5gG_UegNJdP8UvX4Drwa7rg
linkProvider ProQuest
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LS8NAEB60CnqxPvHtHvQYTbLbzeYgIlZpsZaCReqp7G42UKhN7Uv8SR78Ff4xZ_NA8OCtB89LQpKZfPPNzO58AKdMaYFEN3K4xNyEMek5AqOEwxWPI-ox5bJMbCJoNkWnE7YW4LM4C2O3VRaYmAJ1lGhbI7-gftoFCkJ-NXx1rGqU7a4WEhqZW9yb9zdM2caX9Sra98z3727bNzUnVxVwhqGYOFFoghhjoAxp7MlYCBVpGRgfnyqmkRTGN5wyrnkUU1dLLxKe1IFX0RwvwWBG8baLsMTQ190SLLXqD63nAvqpDf_pgFafO5h6-FkbFRmSyy70i7HjwX16bmmHFUn-Bf5pRLsr_7NvsQ5rOXUm15mvb8CCGWxCuZClIDlKbcFTNd2VQpKYSJIt5xVPUjPTbDI1mSTkMenPDEECTJrZvBA5Io3e18cAfxpSzYVjEAD7xArG9behPY-X24HSIBmYXSCqopBFItUzgWS8YgSTUrhKKMyFjYr1HhwWdurmEDDu_hhp_-_lE1iptR8a3Ua9eX8Aq9ZFshrPIZQmo6k5gmU9m_TGo-Pc3Qh052zUb_o6FeI
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=Design+of+a+Computational+Heuristic+to+Solve+the+Nonlinear+Li%C3%A9nard+Differential+Model&rft.jtitle=Computer+modeling+in+engineering+%26+sciences&rft.au=Li%2C+Yan&rft.au=Sabir%2C+Zulqurnain&rft.au=Ilhan%2C+Esin&rft.au=Asif%2C+Muhammad&rft.date=2023-01-01&rft.pub=Tech+Science+Press&rft.issn=1526-1492&rft.eissn=1526-1506&rft.volume=136&rft.issue=1&rft.spage=201&rft_id=info:doi/10.32604%2Fcmes.2023.025094
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1526-1492&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1526-1492&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1526-1492&client=summon