Artificial Neural Networks Ann
Mostrando 13-24 de 105 artigos, teses e dissertações.
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13. 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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14. Influence of film coefficient during multicomponent diffusion – KCl/NaCl in biosolid for static and agitated system using 3D computational simulation
Abstract The influence of film coefficient formed during the diffusion of inorganic salts (NaCl and KCl) in biosolids was studied using a 3D computer modeling by Finite Elements Method (FEM) in COMSOL Multiphysics® software combined with SOM-type Artificial Neural Networks (ANN). Such tools have shown that the influence of the film formed in the biosolid/s
Food Sci. Technol. Publicado em: 20/12/2018
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15. Estimation of the Retention and Availability of Water in Soils of the State of Santa Catarina
ABSTRACT: Soil water retention and availability are important properties for agricultural production, which can be measured directly or estimated by pedotransfer functions. Some studies on this topic were carried out in Santa Catarina, Brazil. To improve the estimates, it is necessary to evaluate other properties, to analyze more soil types, as well as to us
Rev. Bras. Ciênc. Solo. Publicado em: 14/11/2018
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16. Power Flow Analysis and Self-recovery of Electrical Energy Distribution Network Using Artificial Neural Networks
ABSTRACT A computational model for self-recovery of electricity distribution network was developed to simulate it, emulated by the IEEE 123 node model. The electrical system considered has automatic switches capable of identifying a momentary failure in the line and finding the best reconfiguration for its reclosing. An artificial neural network (ANN), backp
Braz. arch. biol. technol.. Publicado em: 29/10/2018
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17. Technical Losses Reduction in Underground Reticulated Distribution Systems using Artificial Neural Networks and Smart Grid Features
ABSTRACT This work presents the methodology, development and testing of an autonomous system, based on Artificial Neural Networks (ANN), for the reduction of technical losses in reticulated underground systems through the optimal control of the capacitor banks (CBs) present in the grid. The proposed methodology includes Smart Grid features, including practic
Braz. arch. biol. technol.. Publicado em: 22/10/2018
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18. Modeling of stem form and volume through machine learning
Abstract Taper functions and volume equations are essential for estimation of the individual volume, which have consolidated theory. On the other hand, mathematical innovation is dynamic, and may improve the forestry modeling. The objective was analyzing the accuracy of machine learning (ML) techniques in relation to a volumetric model and a taper function f
An. Acad. Bras. Ciênc.. Publicado em: 18/10/2018
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19. Artificial neural networks modeling of kinetic curves of celeriac (Apium graveolens L.) in vacuum drying
Abstract The objective of this study was to predict celeriac drying curves using artificial neural networks (ANNs). The experimental data for vacuum drying kinetics of celeriac slices reported by other researcher in the previously published article was used. The air temperature, chamber pressure and time values were used as ANN inputs. To predict the moistu
Food Sci. Technol. Publicado em: 30/07/2018
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20. COMPARISON OF RESPONSE SURFACE METHODOLOGY (RSM) AND ARTIFICIAL NEURAL NETWORKS (ANN) TOWARDS EFFICIENT OPTIMIZATION OF FLEXURAL PROPERTIES OF GYPSUM-BONDED FIBERBOARDS
ABSTRACT In this study, the hydration behavior of gypsum paste mixed with bagasse and kenaf fibers as lignocellulosic material and fiberglass as inorganic material is evaluated. Moreover, the properties of gypsum-bonded fiberboard (GBFB) are examined using bagasse fibers (Saccharum officinarum.L), kenaf fibers (Hibiscus cannabinus.L) and industrial fiberglas
CERNE. Publicado em: 2018-03
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21. 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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22. Artificial neural networks and linear discriminant analysis in early selection among sugarcane families
Abstract One of the major challenges in sugarcane breeding programs is an efficient selection of genotypes in the initial phase. The purpose of this study was to compare modelling by artificial neural networks (ANN) and linear discriminant analysis (LDA) as alternatives for the selection of promising sugarcane families based on the indirect traits number of
Crop Breed. Appl. Biotechnol.. Publicado em: 2017-12
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23. 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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24. Development of a skateboarding trick classifier using accelerometry and machine learning
Abstract Introduction Skateboarding is one of the most popular cultures in Brazil, with more than 8.5 million skateboarders. Nowadays, the discipline of street skating has gained recognition among other more classical sports and awaits its debut at the Tokyo 2020 Summer Olympic Games. This study aimed to explore the state-of-the-art for inertial measurement
Res. Biomed. Eng.. Publicado em: 2017-10