Data/hora: 07/06/2024 01:34:20

Biblioteca(s): | Embrapa Agricultura Digital; Embrapa Pecuária Sul.
Tipo da produção científica: | Artigo em Periódico Indexado
Circulação/Nível: | B - 4
Data corrente: | 09/01/2018
Data da última atualização: | 07/01/2020
Autoria: | FERREIRA, L. M.; SILVA, J. de A.; SANT'ANA, G. C.; CANÇADO, G. M. de A.; BORÉM, A.; FERREIRA, J. L.
Afiliação: | LEILA MARIA FERREIRA, Ufla; JANAÍNA DE ANDRADE SILVA, UFJF; GUSTAVO CÉSAR SANT’ANA, Agronomic Research for Development, France; GERALDO MAGELA DE ALMEIDA CANCADO, CNPTIA; ALUÍZIO BORÉM, UFV; JULIANO LINO FERREIRA, CPPSUL.
Título: | Application of artificial neural networks in the simulation with genetic data.
Ano de publicação: | 2017
Fonte/Imprenta: | International Journal of Engineering Inventions, v. 6, n. 12, p. 43-46, Dec. 2017.
Idioma: | Inglês
Conteúdo: | Abstract: The objective of this work was the concept of applying artificial neural networks in the study of genetic data, in order to make the identification of the microsatellite markers for a particular species of plant to be analyzed more efficient. In this study, was used as an experimental model the data generated for 26 grapevine genotypes were divided into the following populations: Vitis vinifera; North American varieties; and intersp ecific hybrid of rootstocks. After the network training was carried out, an error rate of 0.0003460 was obtained, concluding that the network was able to learn according to the type of data used, even when these data are small.
Palavras-Chave: | Grapevine; Network; Redes neurais.
Thesagro: | Genética; Genótipo.
Thesaurus Nal: | Genotype; Neural networks.
Categoria do assunto: | X Pesquisa, Tecnologia e Engenharia
Registro original: | Embrapa Agricultura Digital (CNPTIA)

Exemplares:

CNPTIA - (19572-1) - UPC - AP - DD -  -  -  - DIS
CPPSUL - (14239-1) - UPC - AP - DD -  -  -  - DIS