Cascaded evolutionary multiobjective identification based on correlation function statistical tests for improving velocity analyzes in swimming
By using biomechanical analyses applied to sports many researchers are providing important information to coaches and athletes in order to reach better performance in a shorter time. In swimming, these kinds of analyses are being used to evaluate, to detect and to improve the skills of high level at...
Uložené v:
| Vydané v: | 2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM) s. 185 - 193 |
|---|---|
| Hlavní autori: | , , , |
| Médium: | Konferenčný príspevok.. |
| Jazyk: | English |
| Vydavateľské údaje: |
IEEE
01.12.2014
|
| Predmet: | |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | By using biomechanical analyses applied to sports many researchers are providing important information to coaches and athletes in order to reach better performance in a shorter time. In swimming, these kinds of analyses are being used to evaluate, to detect and to improve the skills of high level athletes. Recently, evolutionary computing theories have been adopted to support swim velocity profile identification. Based on velocity profiles recognition, it is possible to identify distinct characteristics and classify swimmers according to their abilities. In this way, this work presents an application of Radial Basis Function Neural Network (RBF-NN) associated to a proposed cascaded evolutionary procedure composed by a genetic and Multiobjective Differential Evolution (MODE) algorithms as optimization method for searching the best fitness within a set of parameters to configure the RBF-NN. The main goal and novelty of the proposed approach is to enable, through the adoption of cascaded multiobjective optimization, the use of correlation based tests in order to select both the model lagged inputs and the associated parameters in a supervised fashion. Finally, the real data of a Brazilian elite female swimmer in crawl and breaststroke styles obtained into a 25 meters swimming pool have been identified by the proposed method. The soundness of the approach is illustrated with the adherence to the model validity tests and the values of the multiple correlation coefficients between 0.95 and 0.93 for two tests for both breaststroke and crawl strokes, respectively. |
|---|---|
| AbstractList | By using biomechanical analyses applied to sports many researchers are providing important information to coaches and athletes in order to reach better performance in a shorter time. In swimming, these kinds of analyses are being used to evaluate, to detect and to improve the skills of high level athletes. Recently, evolutionary computing theories have been adopted to support swim velocity profile identification. Based on velocity profiles recognition, it is possible to identify distinct characteristics and classify swimmers according to their abilities. In this way, this work presents an application of Radial Basis Function Neural Network (RBF-NN) associated to a proposed cascaded evolutionary procedure composed by a genetic and Multiobjective Differential Evolution (MODE) algorithms as optimization method for searching the best fitness within a set of parameters to configure the RBF-NN. The main goal and novelty of the proposed approach is to enable, through the adoption of cascaded multiobjective optimization, the use of correlation based tests in order to select both the model lagged inputs and the associated parameters in a supervised fashion. Finally, the real data of a Brazilian elite female swimmer in crawl and breaststroke styles obtained into a 25 meters swimming pool have been identified by the proposed method. The soundness of the approach is illustrated with the adherence to the model validity tests and the values of the multiple correlation coefficients between 0.95 and 0.93 for two tests for both breaststroke and crawl strokes, respectively. |
| Author | Hultmann Ayala, Helon Vicente Ferreira da Cruz, Luciano Zanetti Freire, Roberto dos Santos Coelho, Leandro |
| Author_xml | – sequence: 1 givenname: Helon Vicente surname: Hultmann Ayala fullname: Hultmann Ayala, Helon Vicente email: helon.ayala@pucpr.br organization: Ind. & Syst. Eng. Grad.Program, PUCPR, Curitiba, Brazil – sequence: 2 givenname: Luciano surname: Ferreira da Cruz fullname: Ferreira da Cruz, Luciano email: luciano.cruz@consultant.volvo.com organization: Ind. & Syst. Eng. Grad.Program, PUCPR, Curitiba, Brazil – sequence: 3 givenname: Roberto surname: Zanetti Freire fullname: Zanetti Freire, Roberto email: roberto.freire@pucpr.br organization: Ind. & Syst. Eng. Grad.Program, PUCPR, Curitiba, Brazil – sequence: 4 givenname: Leandro surname: dos Santos Coelho fullname: dos Santos Coelho, Leandro email: leandro.coelho@pucpr.br organization: Dept. of Electr. Eng., PUCPR, Curitiba, Brazil |
| BookMark | eNotkE1OwzAQhY0EC1o4AGLjC7T4r3G8ROFXasUCFuyqiTNBRoldxU5QuQRXxtCu5tOb90Z6MyOnPngk5IqzJefM3Gyqu81SMK6WmjEtWHFCZlxpY5Qq9Ps5-akgWmiwoTiFbkwueBj2tB-7jPUn2uQmpK5Bn1zrLPwZaA0xBzLYMAzYHcR29PYfYspCTNnc0YQxRdqGgbp-N4TJ-Q86YResS3sKHrr9N0bqcujL9X3eXpCzFrqIl8c5J68P92_V02L98vhc3a4XzrC0sEpwUZpWy1IVKJhSZd1AUQAw4MpII8GalSqhNqjFStQSjOSq0WhYI1o5J9eHqw4Rt7vB9bn09vgh-Qv2TGTo |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/MCDM.2014.7007206 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume IEEE Xplore All Conference Proceedings IEEE Electronic Library (IEL) IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| EISBN | 147994467X 9781479944675 |
| EndPage | 193 |
| ExternalDocumentID | 7007206 |
| Genre | orig-research |
| GroupedDBID | 6IE 6IL CBEJK RIE RIL |
| ID | FETCH-LOGICAL-i90t-c421289f73846e20448bda66aa0a149393ac9548ab9e7252b3a9314d7e90d2f3 |
| IEDL.DBID | RIE |
| IngestDate | Thu Jun 29 18:37:41 EDT 2023 |
| IsPeerReviewed | false |
| IsScholarly | false |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i90t-c421289f73846e20448bda66aa0a149393ac9548ab9e7252b3a9314d7e90d2f3 |
| PageCount | 9 |
| ParticipantIDs | ieee_primary_7007206 |
| PublicationCentury | 2000 |
| PublicationDate | 2014-Dec. |
| PublicationDateYYYYMMDD | 2014-12-01 |
| PublicationDate_xml | – month: 12 year: 2014 text: 2014-Dec. |
| PublicationDecade | 2010 |
| PublicationTitle | 2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM) |
| PublicationTitleAbbrev | MCDM |
| PublicationYear | 2014 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| Score | 1.5648072 |
| Snippet | By using biomechanical analyses applied to sports many researchers are providing important information to coaches and athletes in order to reach better... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 185 |
| SubjectTerms | Correlation differential evolution Genetic algorithms Multiobjective optimization Optimization RBF neural networks Sociology swim profile Time series analysis time series forecasting Training |
| Title | Cascaded evolutionary multiobjective identification based on correlation function statistical tests for improving velocity analyzes in swimming |
| URI | https://ieeexplore.ieee.org/document/7007206 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LS8NAEF5q8eBJpRXfzMGjabdJ2s2eq8VLS0EPvZV9TCBiW-lL9E_4l53ZxIrgxduQB4HZJfPNzjffCHGTMqYgoBo5NJSgGMyijGBRRLmEzWNrdexkGDahRqNsMtHjmrjd9cIgYiCfYYvNUMv3C7fho7K2Yp1r1tfeU6pX9mpVhcqO1O1h_27IXK20VT33a2BKiBeDw_996Ug0fxrvYLwLKceihvOG-OybFbPYPeC22ihm-Q6BCriwz-UfCwpf8X6Cq4GjkwcyHI_fKAlvwEEsGNxGFBSazQsQ1lyvgLArFN8HDMBEIkf4HAxrlnzgCgp66a2YzehuUzwO7p_6D1E1RyEqtFxHjou-mc5VQlgDY0kJmfWm1zNGGsqPEp0Yx7JvxmpUcTe2idFJJ_UKtfRxnpyI-nwxx1MBVmKcS-PyjksJZmVZ12npbE6YErW26kw02JXT11IoY1p58fzvyxfigFer5IZcivp6ucErse-25IDldVjdL_8vrUs |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LS8NAEF5KFfSk0opv9-DRtNtN2mTP1VKxLQV76K3sYwIR20pfon_Cv-zMJlYEL96GPAjMLplvdr75hrGbiDAFAtXAgsYERUMSJAiLAswlTCqNUdIKP2wiHgyS8VgNS-x22wsDAJ58BjUyfS3fze2ajsrqMelck772TjOKpMi7tYpSZUOoer991ye2VlQrnvw1MsVHjM7B_751yKo_rXd8uA0qR6wEswr7bOsl8dgdh02xVfTinXsy4Nw85_8snrmC-eOdzSk-OY6GpQEcOeWNUxjzBjUSeY1m_cIRba6WHNErz76PGDhRiSwidK5JteQDljzDl96y6RTvVtlT537U7gbFJIUgU2IVWCr7JiqNQ0QbIAWmZMbpVktroTFDClWoLQm_aaMglk1pQq3CRuRiUMLJNDxm5dl8BieMGwEyFdqmDRsh0EqSplXCmhRRJShl4lNWIVdOXnOpjEnhxbO_L1-zve6o35v0HgaP52yfVi5nilyw8mqxhku2azfojMWVX-kvJuuwkg |
| 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%3Abook&rft.genre=proceeding&rft.title=2014+IEEE+Symposium+on+Computational+Intelligence+in+Multi-Criteria+Decision-Making+%28MCDM%29&rft.atitle=Cascaded+evolutionary+multiobjective+identification+based+on+correlation+function+statistical+tests+for+improving+velocity+analyzes+in+swimming&rft.au=Hultmann+Ayala%2C+Helon+Vicente&rft.au=Ferreira+da+Cruz%2C+Luciano&rft.au=Zanetti+Freire%2C+Roberto&rft.au=dos+Santos+Coelho%2C+Leandro&rft.date=2014-12-01&rft.pub=IEEE&rft.spage=185&rft.epage=193&rft_id=info:doi/10.1109%2FMCDM.2014.7007206&rft.externalDocID=7007206 |