scholarly journals MODELAGEM DA ALTURA E DO INCREMENTO EM ÁREA TRANSVERSAL DE LOURO PARDO

Nativa ◽  
2018 ◽  
Vol 6 (2) ◽  
pp. 191
Author(s):  
Ana Claudia da Silveira ◽  
Luis Paulo Baldissera Schorr ◽  
Elisabete Vuaden ◽  
Jéssica Talheimer Aguiar ◽  
Tarik Cuchi ◽  
...  

O estudo teve como objetivo verificar a melhor técnica para a modelagem da altura e do incremento periódico anual em área transversal para Cordia trichotoma (Vell.) Arrab. ex Steud. Para isso, foram identificados e mensurados os diâmetros à altura do peito e as alturas totais de 35 indivíduos localizados em área de preservação permanente e de pastagem, com aproximadamente 4 ha, no município de Salto do Lontra, estado do Paraná. Posteriormente, foi realizada a análise de tronco pelo método não destrutivo verificando o incremento dos últimos 5 anos. Para a estimativa da altura e do incremento periódico anual em área transversal utilizou-se a técnica dos Modelos Lineares Generalizados (MLG) nas distribuições Gamma, Normal e Poisson nas funções de ligação identidade e logarítmica e Redes Neurais Artificiais (RNA) do tipo Multilayer Perceptron. Para comparação e escolha da melhor técnica, utilizou-se a correlação entre os valores observados e estimados, a raiz quadrada do erro médio e a análise gráfica dos resíduos. Os resultados mostraram que dentre os modelos de MLG, a distribuição Gamma função logarítmica foi indicada para modelagem da altura, ao passo que a distribuição Gamma função identidade foi a recomendada para a modelagem do incremento periódico em área transversal. Quando comparadas as duas técnicas evidenciou-se melhores resultados com a utilização das RNAs, as quais estimaram as variáveis estudadas com maior precisão.Palavra-chave: Cordia trichotoma, modelos lineares generalizados, redes neurais artificiais. MODELING HEIGHT AND TRANVERSAL AREA INCREMENT OF LOURO PARDO ABSTRACT:The present study aimed to verify the best technique for modeling height and annual periodic increment in transversal area for Cordia trichotoma (Vell.) Arrab. Ex Steud. For this purpose, we identify and measured the diameter at breast height and the total height of 35 individuals of this species which located in a permanent preservation and pasture area with approximately 4 hectares, in the municipality of Salto do Lontra, Paraná State, Brazil. Subsequently, the trunk analysis was performed by the non-destructive method, verifying the increment of the last 5 years. For the estimation of height and periodic annual increment in the transversal area, the Generalized Linear Models (MLG) technique was used in the Gamma, Normal and Poisson distributions in the identity and logarithmic functions and Artificial Neural Networks (RNA) of the Multilayer Perceptron type. For comparison and choice of the best technique, the correlation between the observed and estimated values, the square root of the mean error and the graphic analysis of the residues were used. The results showed that among the MLG models, the Gamma distribution with the logarithmic function was indicated for modeling height, whereas the Gamma with identity function was recommended for modeling periodic increment in transversal area. When we compared the two techniques, better results were obtained with the use of RNAs, which estimated the variables studied with greater accuracy.Keywords: Cordia trichotoma, generalized linear models, artificial neural networks. DOI:

2016 ◽  
Vol 19 (1) ◽  
pp. 49-59 ◽  
Author(s):  
Nina Pavlin-Bernardić ◽  
◽  
Silvija Ravić ◽  
Ivan Pavao Matić ◽  
◽  
...  

Artificial neural networks have a wide use in the prediction and classification of different variables, but their application in the area of educational psychology is still relatively rare. The aim of this study was to examine the accuracy of artificial neural networks in predicting students’ general giftedness. The participants were 221 fourth grade students from one Croatian elementary school. The input variables for artificial neural networks were teachers’ and peers’ nominations, school grades, earlier school readiness assessment and parents’ education. The output variable was the result on the Standard Progressive Matrices (Raven, 1994), according to which students were classified as gifted or non-gifted. We tested two artificial neural networks’ algorithms: multilayer perceptron and radial basis function. Within each algorithm, a number of different types of activation functions were tested. 80% of the sample was used for training the network and the remaining 20% to test the network. For a criterion according to which students were classified as gifted if their result on the Standard Progressive Matrices was in the 95th centile or above, the best model was obtained by the hyperbolic tangent multilayer perceptron, which had a high accuracy of 100% of correctly classified non-gifted students and 75% correctly classified gifted students in the test sample. When the criterion was the 90th centile or above, the best model was also obtained by the hyperbolic tangent multilayer perceptron, but the accuracy was lower: 94.7% in the classification of non-gifted students and 66.7% in the classification of gifted students. The study has shown artificial neural networks’ potential in this area, which should be further explored. Keywords: gifted students, identification of gifted students, artificial neural networks


2014 ◽  
Vol 13 ◽  
Author(s):  
Amaury De Souza ◽  
Hamilton Germano Pavão ◽  
Ana Paula Garcia Oliveira

A estimativa da concentração do ozônio de superfície propicia a geração de dados para o planejamento de previsão da qualidade do ar, útil na gestão de saúde publica. O objetivo deste trabalho foi elaborar uma Rede Neural Artificial (RNAs) para estimar a concentração do ozônio de superfície em função de dados diários de clima. A RNA, do tipo FeedForward Multilayer Perceptron, foi treinada tomando-se por referência da concentração diária do ozônio medida. Nas camadas intermediárias e de saída foram utilizadas funções de ativação do tipo tan-sigmóide e lineares, respectivamente. O desempenho da RNA desenvolvida foi muito bom, podendo-se considerá-la como integrante do conjunto de métodos indiretos para estimativa da concentração do ozônio de superfície. O modelo proposto pode ser utilizado pelo governo público como ferramenta para ativar ações de ferramentas durante os períodos de estagnação atmosférica, quando os níveis de ozônio na atmosfera possam representar riscos à saúde publica.


2020 ◽  
Vol 92 (18) ◽  
pp. 12265-12272
Author(s):  
Fabricio A. Chiappini ◽  
Franco Allegrini ◽  
Héctor C. Goicoechea ◽  
Alejandro C. Olivieri

Agriculture ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 567
Author(s):  
Jolanta Wawrzyniak

Artificial neural networks (ANNs) constitute a promising modeling approach that may be used in control systems for postharvest preservation and storage processes. The study investigated the ability of multilayer perceptron and radial-basis function ANNs to predict fungal population levels in bulk stored rapeseeds with various temperatures (T = 12–30 °C) and water activity in seeds (aw = 0.75–0.90). The neural network model input included aw, temperature, and time, whilst the fungal population level was the model output. During the model construction, networks with a different number of hidden layer neurons and different configurations of activation functions in neurons of the hidden and output layers were examined. The best architecture was the multilayer perceptron ANN, in which the hyperbolic tangent function acted as an activation function in the hidden layer neurons, while the linear function was the activation function in the output layer neuron. The developed structure exhibits high prediction accuracy and high generalization capability. The model provided in the research may be readily incorporated into control systems for postharvest rapeseed preservation and storage as a support tool, which based on easily measurable on-line parameters can estimate the risk of fungal development and thus mycotoxin accumulation.


2016 ◽  
Vol 14 (1) ◽  
pp. 309-313 ◽  
Author(s):  
Ivo Mario Mathias ◽  
Luiz Antonio Zanlorensi Junior ◽  
Luciano Bueno Matyak ◽  
Ariangelo Hauer Dias ◽  
Robson Fernando Duda ◽  
...  

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