Artificial Neurons
Mostrando 1-12 de 68 artigos, teses e dissertações.
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1. PREDICTING THE PERFORMANCE PARAMETERS OF CHISEL PLOW USING NEURAL NETWORK MODEL
ABSTRACT This study examines the capability of an artificial neural network (ANN) approach using a backpropagation-learning algorithm to predict performance parameters for a chisel plow at three field sites with differing soils. The draft force, effective field capacity (EFC), fuel consumption rate (FC), overall energy efficiency (OEE), and rate of plowed so
Eng. Agríc.. Publicado em: 2020-12
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2. Application of artificial neural networks in the prediction of sugarcane juice Pol
RESUMO Técnicas inovadoras que busquem minimizar os custos de produção e a onerosidade de determinadas operações são um dos grandes desafios atualmente no setor sucroenergético. Nesse sentido, objetivou-se estimar os valores do Pol do caldo da cana-de-açúcar, em função do °Brix e do peso do bolo úmido (PBU), utilizando modelagem por redes neurai
Rev. bras. eng. agríc. ambient.. Publicado em: 2019-01
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3. A smartphone-based apple yield estimation application using imaging features and the ANN method in mature period
ABSTRACT: Apple yield estimation using a smartphone with image processing technology offers advantages such as low cost, quick access and simple operation. This article proposes distribution framework consisting of the acquisition of fruit tree images, yield prediction in smarphone client, data processing and model calculation in server client for estimating
Sci. agric. (Piracicaba, Braz.). Publicado em: 2018-08
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4. THE USE OF ARTIFICIAL INTELLIGENCE FOR ESTIMATING SOIL RESISTANCE TO PENETRATION
ABSTRACT The aim of this study was to present and to evaluate methodologies for the estimation of soil resistance to penetration (RP) using artificial intelligence prediction techniques. In order to do so, a data base with values of physical-water characteristics of the soils available in the literature was used, and the performances of Artificial Neural Net
Eng. Agríc.. Publicado em: 2018-01
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5. REFERENCE EVAPOTRANSPIRATION FORECASTING BY ARTIFICIAL NEURAL NETWORKS
ABSTRACT: Evapotranspiration (ET) is the main component of water balance in agricultural systems and the most active variable of the hydrological cycle. In the literature, few studies have used the forecast the day before via Artificial Neural Networks (ANNs) for the northern region of São Paulo state, Brazil. Therefore, this aimed to predict the reference
Eng. Agríc.. Publicado em: 2017-12
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6. ESTUDO CINÉTICO DE DECOMPOSIÇÃO TÉRMICA DE ESPUMAS RÍGIDAS DE POLIURETANO POR REDE NEURAL ARTIFICIAL
Kinetic models of solid thermal decomposition are traditionally used for individual fit of isothermal experimental data. However, this methodology presents unacceptable errors in some regions of the data. To solve this problem, a neural network was adopted in this work. The implemented algorithm uses the rate constants as predetermined weights between the in
Quím. Nova. Publicado em: 2017-10
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7. MODELADO DE PARTÍCULAS PM10 Y PM2.5 MEDIANTE REDES NEURONALES ARTIFICIALES SOBRE CLIMA TROPICAL DE SAN FRANCISCO DE CAMPECHE, MÉXICO
In this paper, a computational methodology based on Artificial Neural Networks (ANN) was developed to estimate the index of PM10 and PM2.5 concentration in air of San Francisco de Campeche city. A three layer ANN architecture was trained using an experimental database composed by days of the week, time of day, ambient temperature, atmospheric pressure, wind
Quím. Nova. Publicado em: 2017-09
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8. Automation in accession classification of Brazilian Capsicum germplasm through artificial neural networks
ABSTRACT Germplasm classification by species requires specific knowledge on/of the culture of interest. Therefore, efforts aimed at automation of this process are necessary for the efficient management of collections. Automation of germplasm classification through artificial neural networks may be a viable and less laborious strategy. The aims of this study
Sci. agric. (Piracicaba, Braz.). Publicado em: 2017-06
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9. ESTIMATION OF FUEL CONSUMPTION IN AGRICULTURAL MECHANIZED OPERATIONS USING ARTIFICIAL NEURAL NETWORKS
ABSTRACT 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 n
Eng. Agríc.. Publicado em: 2017-02
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10. Prediction of mean surface temperature of broiler chicks and load microclimate during transport
ABSTRACT This study aimed to determine a model to predict mean surface temperature of broiler chicks and live load microclimate conditions during transport by using neural networks. The research was conducted in the state of São Paulo, Brazil, by monitoring nine shipments with different density of boxes using an air-conditioned truck with an average capacit
Eng. Agríc.. Publicado em: 2016-08
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11. Application of Artificial Neural Networks for Fog Forecast
ABSTRACT: This study examines the development of a system that assists in planning flight activities of the Academia da Força Aérea (AFA) so that meteorological data can be used to predict the occurrence of fog. This system was developed in MATLAB 8.0 by applying multilayer perceptron-type artificial neural networks and using an error correction algorithm
J. Aerosp. Technol. Manag.. Publicado em: 2015-06
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12. Editorial
The objective of this study was to predict by means of Artificial Neural Network (ANN), multilayer perceptrons, the texture attributes of light cheesecurds perceived by trained judges based on instrumental texture measurements. Inputs to the network were the instrumental texture measurements of light cheesecurd (imitative and fundamental parameters). Output
Food Sci. Technol. Publicado em: 2013-12