| 1. Identificação | ||||
| Tipo de Referência | Artigo em Evento (Conference Proceedings) | |||
| Site | mtc-m16d.sid.inpe.br | |||
| Código do Detentor | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S | |||
| Identificador | 8JMKD3MGP7W/3CCBRSB | |||
| Repositório | sid.inpe.br/mtc-m19/2012/07.30.13.09 | |||
| Última Atualização | 2012:08.30.11.40.24 (UTC) administrator | |||
| Repositório de Metadados | sid.inpe.br/mtc-m19/2012/07.30.13.09.24 | |||
| Última Atualização dos Metadados | 2018:06.05.04.12.32 (UTC) administrator | |||
| Chave Secundária | INPE--PRE/ | |||
| DOI | 10.1109/IJCNN.2012.6252622 | |||
| ISBN | 13: 9781467314909 | |||
| Chave de Citação | PereiraPetr:2012:DaAsUs | |||
| Título | Data Assimilation using NeuroEvolution of Augmenting Topologies  | |||
| Formato | On-line | |||
| Ano | 2012 | |||
| Data de Acesso | 26 abr. 2024 | |||
| Tipo Secundário | PRE CI | |||
| Número de Arquivos | 1 | |||
| Tamanho | 944 KiB | |||
| 2. Contextualização | ||||
| Autor | 1 Pereira, André Grahl 2 Petry, Adriano | |||
| Grupo | 1 2 CRS-CCR-INPE-MCTI-GOV-BR | |||
| Afiliação | 1 Computer Science Post-graduate Program - PPGCC, Federal University of Rio Grande do Sul - UFRGS, Porto Alegre, Brazil 2 Instituto Nacional de Pesquisas Espaciais (INPE) | |||
| Nome do Evento | Annual International Joint Conference on Neural Networks (IJCNN). | |||
| Localização do Evento | Brisbane | |||
| Data | 10-15 June 2012 | |||
| Páginas | Article number: 6252622 | |||
| Título do Livro | Proceedings | |||
| Organização | IEEE Computational Intelligence Society (CIS); International Neural Network Society (INNS); IEEE World Congress on Computational Intelligence (WCCI) | |||
| Histórico (UTC) | 2013-01-21 12:40:39 :: marciana -> administrator :: 2012 2018-06-05 04:12:32 :: administrator -> marciana :: 2012 | |||
| 3. Conteúdo e estrutura | ||||
| É a matriz ou uma cópia? | é a matriz | |||
| Estágio do Conteúdo | concluido | |||
| Transferível | 1 | |||
| Tipo do Conteúdo | External Contribution | |||
| Tipo de Versão | publisher | |||
| Palavras-Chave | Backpropagation Biological neural networks Computational modeling Data assimilation Network topology Numerical models Topology | |||
| Resumo | The 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. | |||
| Área | GEST | |||
| Arranjo | urlib.net > BDMCI > Fonds > Produção anterior à 2021 > CRCRS > Data Assimilation using... | |||
| Conteúdo da Pasta doc | acessar | |||
| Conteúdo da Pasta source | não têm arquivos | |||
| Conteúdo da Pasta agreement |
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| 4. Condições de acesso e uso | ||||
| URL dos dados | http://urlib.net/ibi/8JMKD3MGP7W/3CCBRSB | |||
| URL dos dados zipados | http://urlib.net/zip/8JMKD3MGP7W/3CCBRSB | |||
| Idioma | en | |||
| Arquivo Alvo | 06252622.pdf | |||
| Grupo de Usuários | marciana | |||
| Grupo de Leitores | administrator marciana | |||
| Visibilidade | shown | |||
| Permissão de Leitura | allow from all | |||
| Permissão de Atualização | não transferida | |||
| 5. Fontes relacionadas | ||||
| Repositório Espelho | sid.inpe.br/mtc-m19@80/2009/08.21.17.02.53 | |||
| Unidades Imediatamente Superiores | 8JMKD3MGPCW/3EUFCFP | |||
| Divulgação | COMPENDEX | |||
| Acervo Hospedeiro | sid.inpe.br/mtc-m19@80/2009/08.21.17.02 | |||
| 6. Notas | ||||
| Notas | The Annual International Joint Conference on Neural Networks, (IJCNN) is part of the IEEE World Congress on Computational Intelligence, WCCI 2012. | |||
| Campos Vazios | archivingpolicy 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 schedulinginformation secondarydate secondarymark serieseditor session shorttitle sponsor subject tertiarymark tertiarytype type url volume | |||
| 7. Controle da descrição | ||||
| e-Mail (login) | marciana | |||
| atualizar | ||||
