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Doctoral Thesis
DOI
10.11606/T.74.2005.tde-03052005-094734
Document
Author
Full name
Flávia Devechio Providelo Meirelles
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
Pirassununga, 2005
Supervisor
Committee
Ferraz, Jose Bento Sterman (President)
Balieiro, Júlio César de Carvalho
Caliri, Antonio
Cardoso, Vera Lucia
Eler, Joanir Pereira
Title in Portuguese
Modelo computacional de um rebanho bovino de corte virtual utilizando simulação Monte Carlo e redes neurais artificiais
Keywords in Portuguese
gado de corte
modelos estocásticos
Nelore
redes neurais artificiais
simulação Monte Carlo
Abstract in Portuguese
Neste trabalho, foram utilizadas duas ferramentas computacionais para fins de auxiliar tomadas de decisões na produção de bovinos de corte, criados de maneira extensivas, em condições de manejo encontrados no Brasil. A primeira parte do trabalho visou à construção de um software utilizando a técnica de Simulação Monte Carlo para analisar características de produção (ganho de peso) e manejo (fertilidade, anestro pós-parto, taxa de natalidade e puberdade). Na segunda parte do trabalho foi aplicada a técnica de Redes Neurais Artificiais para classificar animais, segundo ganho de peso nas fases de crescimento (nascimento ao desmame, do desmame ao sobreano) relacionado com o valor genético do ganho de peso do desmame ao sobreano (GP345) obtidos pelo BLUP. Ambos modelos mostraram potencial para auxiliar a produção de gado de corte
Title in English
Computational model of virtual beef cattle heard applying Monte Carlo simulation and artificial neural networks
Keywords in English
artificial neural network
beef cattle
Monte Carlo simulation
Nelore
stochastic models
Abstract in English
Herein we applied two different computational techniques with the specific objective to help the decision-making at Brazilian extensive beef cattle production systems. The first part of the work was dedicated to the construction of software based on Monte Carlo Simulation. Two different models were designed for further fusion and willing the analysis of productions (weight gain) and reproduction traits (fertility, post partum anestrus, born rate and puberty). The second part of the work applied Artificial Neural Network techniques to classify animals related to the weight gain during growing period (Weight at Calving, Weaning Weight, Weight at 550 days) comparing data with genetic value of the daily gain from weaning to 550 days adjusted to 345 days BLUP output. The results obtained in both models showed potential to help beef cattle production
 
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3048339.pdf (3.37 Mbytes)
Publishing Date
2005-05-03
 
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