Neural Networks Optimization
Mostrando 1-12 de 88 artigos, teses e dissertações.
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1. Discrimination of pores and cracks in iron ore pellets using deep learning neural networks
Abstract The discrimination between pores and cracks is an important step in the microstructural analysis of iron ore pellets. While the porosity is fundamental during the reduction process in blast furnaces, cracks are strongly detrimental to the mechanical strength. The usual image processing tools cannot automatically discriminate between these two types
REM, Int. Eng. J.. Publicado em: 2020-06
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2. ARTIFICIAL NEURAL NETWORKS APPLIED IN FOREST BIOMETRICS AND MODELING: STATE OF THE ART (JANUARY/2007 TO JULY/2018)
ABSTRACT Artificial Intelligence has been an important support tool in different spheres of activity, enabling knowledge aggregation, process optimization and the application of methodologies capable of solving complex real problems. Despite focusing on a wide range of successful metrics, the Artificial Neural Network (ANN) approach, a technique similar to t
CERNE. Publicado em: 09/09/2019
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3. 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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4. Design of Waveguide Structures Using Improved Neural Networks
Abstract In this paper, an improved neural networks (INN) strategy is proposed to design two waveguide filters (Pseudo-elliptic waveguide filter and Broad-band e-plane filters with improved stop-band). INN is trained by an efficient optimization algorithm called teaching–learning-based optimization (TLBO). To validate the effective of this proposed strateg
J. Microw. Optoelectron. Electromagn. Appl.. Publicado em: 2017-12
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5. 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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6. Use of an Artificial Neural Network in determination of iron ore pellet bed permeability
Abstract The thermal processing of iron ore pellets in pelletizing plants is a decisive stage regarding final product quality and knowledge of its characteristics has a fundamental importance in its process optimization. This study evaluated the variable sensitivity involved in pellet bed formations and their permeability using the artificial neural networks
REM, Int. Eng. J.. Publicado em: 2017-06
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7. MACHINE LEARNING TECHNIQUES APPLIED TO LIGNOCELLULOSIC ETHANOL IN SIMULTANEOUS HYDROLYSIS AND FERMENTATION
Abstract This paper investigates the use of machine learning (ML) techniques to study the effect of different process conditions on ethanol production from lignocellulosic sugarcane bagasse biomass using S. cerevisiae in a simultaneous hydrolysis and fermentation (SHF) process. The effects of temperature, enzyme concentration, biomass load, inoculum size and
Braz. J. Chem. Eng.. Publicado em: 2017-01
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8. Developing and Multi-Objective Optimization of a Combined Energy Absorber Structure Using Polynomial Neural Networks and Evolutionary Algorithms
Abstract In this study a newly developed thin-walled structure with the combination of circular and square sections is investigated in term of crashworthiness. The results of the experimental tests are utilized to validate the Abaqus/ExplicitTM finite element simulations and analysis of the crush phenomenon. Three polynomial meta-models based on the evolved
Lat. Am. j. solids struct.. Publicado em: 2016
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9. A hybrid model of uniform design and artificial neural network for the optimization of dietary metabolizable energy, digestible lysine, and methionine in quail chicks
A uniform design (UD) was used to construct models to explain the growth response of Japanese quails to dietary metabolizable energy (ME), and digestible methionine (dMet) and lysine (dLys) under tropical condition. In total, 100 floor pens with seven birds each were fed 25 UD different diets containing 25 ME (2808-3092 kcal/kg), dMet (0.31-0.49% of diet), a
Rev. Bras. Cienc. Avic.. Publicado em: 2014-09
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10. Time-series forecasting of pollutant concentration levels using particle swarm optimization and artificial neural networks
This study evaluates the application of an intelligent hybrid system for time-series forecasting of atmospheric pollutant concentration levels. The proposed method consists of an artificial neural network combined with a particle swarm optimization algorithm. The method not only searches relevant time lags for the correct characterization of the time series,
Quím. Nova. Publicado em: 2013
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11. Redes neurais com topologias otimizadas aplicadas na modelagem de dados geotécnicos e pluviométricos para predição de deslizamentos de solo
The problems related to landslides caused by rain on slopes have been a reality in several cities of Brazil and parts of the world. The government and research institutions have sought, in partnership, solutions to minimize the damage caused by such accidents, from studies of correlation between rainfall and landslide occurrences enabling the development of
IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia. Publicado em: 28/06/2012
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12. Estimação e previsão da estrutura a termo das taxas de juros usando técnicas de inteligência computacional / Term structure of interest rate modeling and forecasting using computational intelligence techniques
This work proposes the term structure of interest rates modeling and forecasting using computational intelligence techniques, based on data from the US and Brazilian fixed income markets. The yield curve modeling includes the use of some evolutionary computation methods like Genetic Algorithms, Differential Evolution and Evolution Strategies in comparison wi
IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia. Publicado em: 25/06/2012