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1. Identity statement
Reference TypeJournal Article
Sitemtc-m16d.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP7W/36HQHE5
Repositorysid.inpe.br/mtc-m19@80/2009/12.10.17.47   (restricted access)
Last Update2009:12.10.17.47.52 (UTC) administrator
Metadata Repositorysid.inpe.br/mtc-m19@80/2009/12.10.17.47.53
Metadata Last Update2018:06.05.04.36.02 (UTC) administrator
Secondary KeyINPE--PRE/
DOI10.1007/s00521-009-0298-3
ISSN0941-0643
Citation KeyLimaSilvSaot:2010:VeInSi
TitleVehicle inductive signatures recognition using a Madaline neural network
Year2010
MonthSept.
Access Date2024, Apr. 28
Secondary TypePRE PI
Number of Files1
Size792 KiB
2. Context
Author1 Lima, Glauston R. Teixeira
2 Silva, José Demísio Simões da
3 Saotome, Osamu
Resume Identifier1
2 8JMKD3MGP5W/3C9JHH2
Group1
2 LAC-CTE-INPE-MCT-BR
Affiliation1 Instituto Tecnológico de Aeronáutica-ITA
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Instituto Tecnológico de Aeronáutica-ITA
JournalNeural Computing and Applications
Volume19
Number3
Pages421 436
Secondary MarkB2_CIÊNCIA_DA_COMPUTAÇÃO A2_ENGENHARIAS_IV B1_INTERDISCIPLINAR
History (UTC)2009-12-10 17:48:24 :: simone -> administrator ::
2010-05-11 01:02:51 :: administrator -> simone ::
2011-09-12 15:53:21 :: simone -> administrator :: 2010
2018-06-05 04:36:02 :: administrator -> marciana :: 2010
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsVehicle inductive signatures - Vehicle classification - Madaline neural network
AbstractIn this paper, we report results obtained with a Madaline neural network trained to classify inductive signatures of two vehicles classes: trucks with one rear axle and trucks with double rear axle. In order to train the Madaline, the inductive signatures were pre-processed and both classes, named C2 and C3, were subdivided into four subclasses. Thus, the initial classification task was split into four smaller tasks (theoretically) easier to be performed. The heuristic adopted in the training attempts to minimize the effects of the input space non-linearity on the classifier performance by uncoupling the learning of the classes and, for this, we induce output Adalines to specialize in learning one of the classes. The percentages of correct classifications presented concern patterns which were not submitted to the neural network in the training process, and, therefore, they indicate the neural network generalization ability. The results are good and stimulate the maintenance of this research on the use of Madaline networks in vehicle classification tasks using not linearly separable inductive signatures.
AreaCOMP
Arrangementurlib.net > BDMCI > Fonds > Produção anterior à 2021 > LABAC > Vehicle inductive signatures...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Contentthere are no files
4. Conditions of access and use
Target Filevehicle indutive.pdf
User Groupadministrator
simone
Visibilityshown
Archiving Policydenypublisher denyfinaldraft12
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/mtc-m19@80/2009/08.21.17.02.53
Next Higher Units8JMKD3MGPCW/3ESGTTP
DisseminationWEBSCI; PORTALCAPES.
Host Collectionsid.inpe.br/mtc-m19@80/2009/08.21.17.02
6. Notes
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7. Description control
e-Mail (login)marciana
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