| 1. Identity statement | ||||
| Reference Type | Conference Paper (Conference Proceedings) | |||
| Site | mtc-m16d.sid.inpe.br | |||
| Holder Code | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S | |||
| Identifier | 8JMKD3MGP7W/3CCBRSB | |||
| Repository | sid.inpe.br/mtc-m19/2012/07.30.13.09 | |||
| Last Update | 2012:08.30.11.40.24 (UTC) administrator | |||
| Metadata Repository | sid.inpe.br/mtc-m19/2012/07.30.13.09.24 | |||
| Metadata Last Update | 2018:06.05.04.12.32 (UTC) administrator | |||
| Secondary Key | INPE--PRE/ | |||
| DOI | 10.1109/IJCNN.2012.6252622 | |||
| ISBN | 13: 9781467314909 | |||
| Citation Key | PereiraPetr:2012:DaAsUs | |||
| Title | Data Assimilation using NeuroEvolution of Augmenting Topologies  | |||
| Format | On-line | |||
| Year | 2012 | |||
| Access Date | 2023, Jan. 31 | |||
| Secondary Type | PRE CI | |||
| Number of Files | 1 | |||
| Size | 944 KiB | |||
| 2. Context | ||||
| Author | 1 Pereira, André Grahl 2 Petry, Adriano | |||
| Group | 1 2 CRS-CCR-INPE-MCTI-GOV-BR | |||
| Affiliation | 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) | |||
| Conference Name | Annual International Joint Conference on Neural Networks (IJCNN). | |||
| Conference Location | Brisbane | |||
| Date | 10-15 June 2012 | |||
| Pages | Article number: 6252622 | |||
| Book Title | Proceedings | |||
| Organization | IEEE 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 Stage | completed | |||
| Transferable | 1 | |||
| Content Type | External Contribution | |||
| Version Type | publisher | |||
| Keywords | Backpropagation Biological neural networks Computational modeling Data assimilation Network topology Numerical models Topology | |||
| Abstract | 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. | |||
| Area | GEST | |||
| Arrangement | ||||
| doc Directory Content | access | |||
| source Directory Content | there are no files | |||
| agreement Directory Content |
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| 4. Conditions of access and use | ||||
| data URL | http://urlib.net/ibi/8JMKD3MGP7W/3CCBRSB | |||
| zipped data URL | http://urlib.net/zip/8JMKD3MGP7W/3CCBRSB | |||
| Language | en | |||
| Target File | 06252622.pdf | |||
| User Group | marciana | |||
| Reader Group | administrator marciana | |||
| Visibility | shown | |||
| Read Permission | allow from all | |||
| Update Permission | not transferred | |||
| 5. Allied materials | ||||
| Mirror Repository | sid.inpe.br/mtc-m19@80/2009/08.21.17.02.53 | |||
| Next Higher Units | 8JMKD3MGPCW/3EUFCFP | |||
| Dissemination | COMPENDEX | |||
| Host Collection | sid.inpe.br/mtc-m19@80/2009/08.21.17.02 | |||
| 6. Notes | ||||
| Notes | The Annual International Joint Conference on Neural Networks, (IJCNN) is part of the IEEE World Congress on Computational Intelligence, WCCI 2012. | |||
| Empty Fields | 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 secondarydate secondarymark serieseditor session shorttitle sponsor subject tertiarymark tertiarytype type url volume | |||
| 7. Description control | ||||
| e-Mail (login) | marciana | |||
| update | ||||
