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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitemtc-m21b.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34P/3K98AQP
Repositorysid.inpe.br/mtc-m21b/2015/09.16.19.29
Last Update2016:01.06.11.19.39 (UTC) administrator
Metadata Repositorysid.inpe.br/mtc-m21b/2015/09.16.19.29.59
Metadata Last Update2018:06.04.02.55.40 (UTC) administrator
Secondary KeyINPE--PRE/
Citation KeyLopesSumCamCamSan:2015:PrReCh
TitleA proposal for regime change/duration classification in chaotic systems
Year2015
Access Date2024, May 17
Secondary TypePRE CI
Number of Files1
Size1308 KiB
2. Context
Author1 Lopes, P. A.
2 Sumida, I. Y.
3 Camargo, H. A.
4 Campos Velho, Haroldo Fraga de
5 Sandri, Sandra Aparecida
Resume Identifier1
2
3
4 8JMKD3MGP5W/3C9JHC3
5 8JMKD3MGP5W/3E3JEJL
Group1
2
3
4 LAC-CTE-INPE-MCTI-GOV-BR
5 LAC-CTE-INPE-MCTI-GOV-BR
Affiliation1 Universidade Federal de São Carlos (UFSCar)
2 Universidade Federal de São Carlos (UFSCar)
3 Universidade Federal de São Carlos (UFSCar)
4 Instituto Nacional de Pesquisas Espaciais (INPE)
5 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1
2
3
4 haroldo.camposvelho@inpe.br
5 sandra.sandri@inpe.br
Conference NameConference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology
Conference LocationGijon, Spain
Date30 June - 03 July
Book TitleProceedings
History (UTC)2015-09-16 19:29:59 :: simone -> administrator ::
2018-06-04 02:55:40 :: administrator -> simone :: 2015
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsChaotic systems
fuzzy clustering
bred vectors
Lorenz attractor
neuro-fuzzy systems
decision trees
AbstractIn order to to predict regime duration in a given chaotic system, for a set of output prototypes are available, we propose to use a clustering technique for the definition of classes of regime duration, which are then used by a chosen classifier. In this way, the exact boundaries between classes are allowed to emerge from the data, as long as prototypical values fall in distinct classes. We investigate the use of both unsupervised and semi-supervised fuzzy clustering techniques FCM and ssFCM, as well as the traditional k-Means technique. To classify the data, we use neuro-fuzzy system ANFIS and two decision trees (J48 and NBTree). We apply the procedure on the well-known Lorenz strange attractor, having bred vector counts as input variables.
AreaCOMP
Arrangementurlib.net > BDMCI > Fonds > Produção anterior à 2021 > LABAC > A proposal for...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
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4. Conditions of access and use
data URLhttp://mtc-m21b.sid.inpe.br/ibi/8JMKD3MGP3W34P/3K98AQP
zipped data URLhttp://mtc-m21b.sid.inpe.br/zip/8JMKD3MGP3W34P/3K98AQP
User Groupsimone
Reader Groupadministrator
simone
Visibilityshown
Read Permissionallow from all
Update Permissionnot transferred
5. Allied materials
Mirror Repositoryurlib.net/www/2011/03.29.20.55
Next Higher Units8JMKD3MGPCW/3ESGTTP
Citing Item Listsid.inpe.br/mtc-m21/2012/07.13.14.49.40 4
Host Collectionsid.inpe.br/mtc-m21b/2013/09.26.14.25.20
6. Notes
Empty Fieldsarchivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel dissemination doi e-mailaddress edition editor format isbn issn label language lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress rightsholder schedulinginformation secondarydate secondarymark serieseditor session shorttitle sponsor subject targetfile tertiarymark tertiarytype type url volume
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