Uso de redes neurais artificais na predição de valores genéticos para peso aos 205 dias em bovinos da raça Tabapuã

Ventura, Ricardo Vieira; Silva, M.A.; Medeiros, T.H.; Dionello, N.L.; Madalena, F. E.; Fridrich, A.B.; Valente, Bruno D.; Santos, G.G.; Freitas, L.S.; Wenceslau, Raphael Rocha; Felipe, V.P.S.; Corrêa, G.S.S.

doi:10.1590/s0102-09352012000200022

Cited by 11 publications

(17 citation statements)

References 2 publications

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“…Based on the studies of TERRA & PASSADOR (2012), VENTURA et al (2012), BINOTI et al (2013, 2014a, b) and VALENTE et al (2014, the artificial neural networks with the "Perceptron" structure with multi-layer (MPL) was chosen to determine fuel consumption in agricultural operations according to the performance criteria. The architecture of the networks was set up according to the suggestions of TERRA & PASSADOR (2012) and VENTURA et al (2012) that is, changing the number of intermediate layers and the number of neurons in the layers.…”

Section: Methodsmentioning

confidence: 99%

“…The architecture of the networks was set up according to the suggestions of TERRA & PASSADOR (2012) and VENTURA et al (2012) that is, changing the number of intermediate layers and the number of neurons in the layers. The profile input of the mobilized soil; the mechanical resistance and the slipping on the drive wheels variables as well as the output fuel consumption variable were normalized to reduce the effects of scale.…”

Section: Methodsmentioning

confidence: 99%

“…According to the suggestions of BINOTI et al (2013BINOTI et al ( , 2014, CANSIAN et al (2014), GEORGENS et al (2014) and VALENTE et al (2014),for training and validation, the data were randomly divided into two sets, corresponding to 75% and 25%, respectively. In all architectures the logistic function to enable the networks was used, and the initial weights randomly generated between -0.5 and 0.5.The layers are interconnected by complete synapses, each neuron of i layer was connected to all neurons of the next layer (i + 1), the synapse being oriented forward only ("feed forward").…”

Section: Methodsmentioning

confidence: 99%

“…However, in these studies, the functional dependencies to determine this consumption were not established. Moreover, the use of artificial neural networks as an alternative to relate variables and parameters of physical and biological processes is growing daily and its effectiveness confirmed by ALVES SOBRINHO et al (2011), PANDORFI et al (2011), CARVALHO et al (2012), VENTURA et al (2012, BINOTI et al (2013BINOTI et al ( , 2014, GEORGENS et al (2014), SOARES et al (2014) and VALENTE et al (2014).…”

Section: Introductionmentioning

confidence: 96%

“…Also, the soil mechanical resistance to penetration was studied by BORGES et al (2014). In this study, the authors used this resistance to describe the current state of the land and developed a statistical model to estimate the tensile strength in discs ploughs, spring tillers and planters.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Fuel Consumption in Agricultural Mechanized Operations Using Artificial Neural Networks

et al. 2017

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This study aimed to develop artificial neural networks for the estimation of tractor fuel consumption during soil preparation, according to the adopted system. The multilayer perceptron network was chosen. As input data: the soil mechanical penetration resistance, the mobilized area by implements, the working gear and the tractor engine speed. The number of layers and neurons varied to form different architectures. The adjustment was verified based on various statistical criteria. The values estimated by the networks did not differ significantly from those obtained experimentally. The conclusion was that the networks showed adequate reliability and accuracy to predicting the fuel consumption in each tillage system, in function of the input data and this can be a useful tool for planning and management of agricultural operations.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Estimation of Fuel Consumption in Agricultural Mechanized Operations Using Artificial Neural Networks

et al. 2017

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Neural networks for predicting breeding values and genetic gains

Silva

Tomaz

Sant’Anna

et al. 2014

Sci. agric. (Piracicaba, Braz.)

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Analysis using Artificial Neural Networks has been described as an approach in the decision-making process that, although incipient, has been reported as presenting high potential for use in animal and plant breeding. In this study, we introduce the procedure of using the expanded data set for training the network. Wealso proposed using statistical parameters to estimate the breeding value of genotypes in simulated scenarios, in addition to the mean phenotypic value in a feed-forward back propagation multilayer perceptron network. After evaluating artificial neural network configurations, our results showed its superiority to estimates based on linear models, as well as its applicability in the genetic value prediction process. The results further indicated the good generalization performance of the neural network model in several additional validation experiments.

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Application of artificial neural networks in indirect selection: a case study on the breeding of lettuce

et al. 2015

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The efficiency of artificial neural networks (ANN) to model complex problems may enable the prediction of characteristics that are hard to measure, providing better results than the traditional indirect selection. Thus, this study aimed to investigate the potential of using artificial neural networks (ANN) for indirect selection against early flowering in lettuce, identify the influence of genotype by environment interaction in this strategy and compare your results with the traditional indirect selection. The number of days to anthesis were used as the desired output and the information of six characteristics (fresh weight of shoots, mass of marketable fresh matter of shoots, commercial dry matter of shoots, average diameter of the head, head circumference and leaf number) as input file for the training of the ANN-MLP (Perceptron Multi-Layer). The use of ANN has great potential adjustment for indirect selection for genetic improvement of lettuce against early flowering. The selection based on the predicted values by network provided estimates of gain selection largest that traditional indirect selection. The ANN trained with data from an experiment have low power extrapolation to another experiment, due to effect of interaction genotype by environment. The ANNs trained simultaneously with data from different experiments presented greater predictive power and extrapolation.

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Uso de redes neurais artificais na predição de valores genéticos para peso aos 205 dias em bovinos da raça Tabapuã

Abstract: Uso de redes neurais artificais na predição de valores genéticos para peso aos 205 dias em bovinos da raça Tabapuã

Cited by 11 publications

References 2 publications

Estimation of Fuel Consumption in Agricultural Mechanized Operations Using Artificial Neural Networks

Estimation of Fuel Consumption in Agricultural Mechanized Operations Using Artificial Neural Networks

Neural networks for predicting breeding values and genetic gains

Application of artificial neural networks in indirect selection: a case study on the breeding of lettuce

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