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The search expression was <secondaryty ci and ref conference and firstg CRS-CCR-INPE-MCTI-GOV-BR and y 2012 and is *>.
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Search local date and time: 31/01/2023 07:35.
1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitemtc-m16d.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP7W/3CCBRSB
Repositorysid.inpe.br/mtc-m19/2012/07.30.13.09
Last Update2012:08.30.11.40.24 (UTC) administrator
Metadata Repositorysid.inpe.br/mtc-m19/2012/07.30.13.09.24
Metadata Last Update2018:06.05.04.12.32 (UTC) administrator
Secondary KeyINPE--PRE/
DOI10.1109/IJCNN.2012.6252622
ISBN13: 9781467314909
Citation KeyPereiraPetr:2012:DaAsUs
TitleData Assimilation using NeuroEvolution of Augmenting Topologies
FormatOn-line
Year2012
Access Date2023, Jan. 31
Secondary TypePRE CI
Number of Files1
Size944 KiB
2. Context
Author1 Pereira, André Grahl
2 Petry, Adriano
Group1
2 CRS-CCR-INPE-MCTI-GOV-BR
Affiliation1 Computer Science Post-graduate Program - PPGCC, Federal University of Rio Grande do Sul - UFRGS, Porto Alegre, Brazil
2 Instituto Nacional de Pesquisas Espaciais (INPE)
Conference NameAnnual International Joint Conference on Neural Networks (IJCNN).
Conference LocationBrisbane
Date10-15 June 2012
PagesArticle number: 6252622
Book TitleProceedings
OrganizationIEEE Computational Intelligence Society (CIS); International Neural Network Society (INNS); IEEE World Congress on Computational Intelligence (WCCI)
History (UTC)2013-01-21 12:40:39 :: marciana -> administrator :: 2012
2018-06-05 04:12:32 :: administrator -> marciana :: 2012
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsBackpropagation
Biological neural networks
Computational modeling
Data assimilation
Network topology
Numerical models
Topology
AbstractThe use of numerical prediction models are essential to modern society. Data assimilation is a technique that aims to increase the prediction accuracy by combining a model output with observational data, resulting in a state that is closer to the true state of the problem. Depending on the size of the model output and the number of observations to assimilate, the combination of these two sources of information may require intensive computing and become a challenge, even for supercomputers used in this type of application. Thus neural networks have been proposed as an alternative to perform high quality data assimilation at lower computational cost. This paper investigates the use of NeuroEvolution of Augmenting Topologies (NEAT) in data assimilation. NEAT is capable of adapting the connections weights and the neural network topology using principles of evolutionary computation in a search for a minimum topology and best performance. In this work, two different models were used for testing: the Lorenz Attractor and Shallow Water model. The experiments compared the results obtained with NEAT and backpropagation neural networks, using as benchmark the Best Linear Unbiased Estimator (BLUE). In the experiment with the Lorenz Attractor, NEAT was able to emulate the data assimilation task with smaller error at lower computational cost. For the Shallow Water model, tested using different grid sizes, it was observed that the errors obtained with both neural networks were small, but NEAT showed high error values. On the other hand, NEAT always gets a topology with significantly fewer operations, and the computational cost difference increases with the grid size.
AreaGEST
Arrangementurlib.net > BDMCI > Fonds > Produção anterior à 2021 > CRCRS > Data Assimilation using...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
agreement.html 30/07/2012 10:09 1.0 KiB 
4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGP7W/3CCBRSB
zipped data URLhttp://urlib.net/zip/8JMKD3MGP7W/3CCBRSB
Languageen
Target File06252622.pdf
User Groupmarciana
Reader Groupadministrator
marciana
Visibilityshown
Read Permissionallow from all
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/mtc-m19@80/2009/08.21.17.02.53
Next Higher Units8JMKD3MGPCW/3EUFCFP
DisseminationCOMPENDEX
Host Collectionsid.inpe.br/mtc-m19@80/2009/08.21.17.02
6. Notes
NotesThe Annual International Joint Conference on Neural Networks, (IJCNN) is part of the IEEE World Congress on Computational Intelligence, WCCI 2012.
Empty Fieldsarchivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress edition editor electronicmailaddress issn label lineage mark nextedition numberofvolumes orcid parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress resumeid rightsholder secondarydate secondarymark serieseditor session shorttitle sponsor subject tertiarymark tertiarytype type url volume
7. Description control
e-Mail (login)marciana
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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Siteplutao.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
IdentifierJ8LNKAN8RW/3D536HU
Repositorydpi.inpe.br/plutao/2012/11.28.13.56.59
Last Update2020:11.16.12.50.47 (UTC) marciana
Metadata Repositorydpi.inpe.br/plutao/2012/11.28.13.57
Metadata Last Update2020:11.16.12.50.47 (UTC) marciana
Secondary KeyINPE--PRE/
ISBN9781467311
Labellattes: 6471662266291019 1 SilvaFreiSantFrer:2012:PoReCl
Citation KeySilvaFreiSantFrer:2012:PoReCl
TitlePolsar region classifier based on stochastic distances and hypothesis tests
FormatOn-line
Year2012
Access Date2023, Jan. 31
Secondary TypePRE CI
Number of Files1
Size998 KiB
2. Context
Author1 Silva, Wagner Barreto da
2 Freitas, Corina da Costa
3 Sant'Anna, Sidnei Joao Siqueira
4 Frery, Alejandro César
Resume Identifier1
2
3 8JMKD3MGP5W/3C9JJ8N
Group1
2
3
4 CRS-CCR-INPE-MCTI-GOV-BR
Affiliation1
2
3
4 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1 wbarreto.w3@gmail.com
2
3 sidnei@dpi.inpe.br
e-Mail Addresswbarreto.w3@gmail.com
Conference NameIEEE International Geoscience and Remote Sensing Symposium, 32 (IGARSS).
Conference LocationMunique
Date2012
Pages1473-1476
Book TitleProceedings
Tertiary TypePaper
History (UTC)2012-11-28 23:06:21 :: lattes -> marciana :: 2012
2012-12-21 18:23:55 :: marciana -> administrator :: 2012
2018-06-05 00:01:54 :: administrator -> marciana :: 2012
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
AreaSRE
Arrangementurlib.net > BDMCI > Fonds > Produção anterior à 2021 > CRCRS > Polsar region classifier...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Contentthere are no files
4. Conditions of access and use
data URLhttp://urlib.net/ibi/J8LNKAN8RW/3D536HU
zipped data URLhttp://urlib.net/zip/J8LNKAN8RW/3D536HU
Languageen
Target Filesilva_polsar.pdf
User Grouplattes
marciana
Visibilityshown
Read Permissionallow from all
Update Permissionnot transferred
5. Allied materials
LinkingTrabalho Vinculado à Tese/Dissertação
Next Higher Units8JMKD3MGPCW/3EUFCFP
URL (untrusted data)http://www.igarss2012.org/Papers/viewpapers.asp?papernum=3295
Host Collectiondpi.inpe.br/plutao@80/2008/08.19.15.01
6. Notes
Empty Fieldsabstract archivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel dissemination doi edition editor issn keywords lineage mark mirrorrepository nextedition notes numberofvolumes orcid organization parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress readergroup rightsholder secondarydate secondarymark serieseditor session shorttitle sponsor subject type volume
7. Description control
e-Mail (login)marciana
update