scholarly journals Previsión del ambiente térmico para el ganado lechero mediante redes neuronales artificiales

2021 ◽  
Vol 1 (4) ◽  
pp. 104-119
Author(s):  
Pedro Hurtado de Mendoza Borges ◽  
Zaíra Morais dos Santos Hurtado De Mendoza ◽  
Pedro Hurtado de Mendoza Morais
Keyword(s):  
Neural Networks ◽  
Dairy Cattle ◽  
Thermal Environment ◽  
Absolute Error ◽  
Mato Grosso ◽  
Historical Series ◽  
Temperature And Humidity ◽  
Humidity Index ◽  
The Mean ◽  
Artificial Neural

El presente estudio tuvo como objetivo la previsión del ambiente térmico para el ganado lechero mediante redes neuronales artificiales, de acordó con la temperatura y humedad diaria. En la investigación se utilizaron los valores diarios de esas variables, disponibles en el Instituto Nacional de Meteorología de Brasil. Los datos correspondieron a las series históricas registradas en estaciones convencionales con tiempo de operación superior a 30 años hasta 2020. A continuación, se seleccionaron los municipios Canarana, Matupá, Nova Xavantina y Santo Antônio de Leverger, localizados en Mato Grosso, Brasil. Con base en los dados climatológicos, se estimó el Índice de Temperatura y Humedad diario en el calendario Juliano. Posteriormente, se probaron 35 arquitecturas de redes neuronales artificiales con tipología perceptrón de múltiples camadas, siendo la variable de entrada el día Juliano y la de salida el Índice de Temperatura y Humedad. La idoneidad de las redes fue verificada por el coeficiente de determinación, el error absoluto medio, el error cuadrático medio, el porciento medio del error absoluto y la normalidad de los residuos. No hubo diferencias entre los valores estimados por las redes y los obtenidos a partir de las series históricas. La rede de mejor desempeño y eficiencia para cada municipio, también fue comprobada por el análisis gráfico de los residuos. Se concluyó que las redes neuronales con tipología perceptron de dos camadas ocultas fueron apropiadas en el pronóstico del ambiente térmico natural para el ganado lechero.   The present study aimed to forecast the thermal environment for dairy cattle through artificial neural networks, according to the daily temperature and humidity. The research used the daily values of these variables, available in the National Institute of Meteorology of  Brazil. The data corresponded to the historical series registered in conventional stations with an operating time of more than 30 years until 2020. Next, the municipalities Canarana, Matupá, Nova Xavantina and Santo Antônio de Leverger, located in Mato Grosso, Brazil, were selected. Based on the climatological data, the Temperature and Humidity Index was determined for each day of the year in the Julian calendar. Subsequently, 35 artificial neural network architectures with multiple layer perceptron typology were tested, the input variable being the Julian day and the output variable being the Temperature and Humidity Index. The suitability of the networks was verified by the coefficient of determination, the mean absolute error, the mean square error, the mean percentage of the absolute error and the normality of the residuals. There were no differences between the values estimated by the networks and those obtained from the historical series. The network with the best performance and efficiency for each municipality was also verified by the graphic analysis of the residuals. It was concluded that the neural networks with perceptron typology of two hidden layers were appropriate in the forecast of the natural thermal environment for dairy cattle.

Prediction by using spectral analysis and artificial neural networks methodologies: Comparison Study

2017 ◽  
Vol 9 (2) ◽  
Author(s):  
Mohammed Habib Al- Sharoot ◽  
Emaan Yousif Abdoon
Keyword(s):  
Neural Networks ◽  
Spectral Analysis ◽  
Artificial Neural Networks ◽  
Exchange Rate ◽  
Absolute Error ◽  
Percentage Error ◽  
The Mean ◽  
Us Dollar ◽  
Artificial Neural ◽  
Stable Economy

The variations in exchange rate, especially the sudden unexpected increases and decreases, have significant impact on the national economy of any country. Iraq is no exception; therefore, the accurate forecasting of exchange rate of Iraqi dinar to US dollar plays an important role in the planning and decision-making processes as well as the maintenance of a stable economy in Iraq. This research aims to compare spectral analysis methodology to artificial neural networks in terms of forecasting the exchange rate of Iraqi dinar to US dollar based on data provided by the Iraqi Central Bank for the period 30/01/2004 and 30/12/2014. Based on the Mean Square Error (MSE), the Mean Absolute Error (MAE), and the Mean Absolute Percentage Error (MAPE) as criteria to compare the two methodologies, it was concluded that is artificial neural networks better than spectral analysis approach in forecasting.

scholarly journals Prediction of Blended Yarn Evenness and Tensile Properties by Using Artificial Neural Network and Multiple Linear Regression

2016 ◽  
Vol 16 (2) ◽  
pp. 43-50 ◽  
Author(s):  
Samander Ali Malik ◽  
Assad Farooq ◽  
Thomas Gereke ◽  
Chokri Cherif
Keyword(s):  
Neural Networks ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Tensile Properties ◽  
Regression Models ◽  
Prediction Models ◽  
Research Work ◽  
Absolute Error ◽  
Knowledge Domain ◽  
Artificial Neural

Abstract The present research work was carried out to develop the prediction models for blended ring spun yarn evenness and tensile parameters using artificial neural networks (ANNs) and multiple linear regression (MLR). Polyester/cotton blend ratio, twist multiplier, back roller hardness and break draft ratio were used as input parameters to predict yarn evenness in terms of CVm% and yarn tensile properties in terms of tenacity and elongation. Feed forward neural networks with Bayesian regularisation support were successfully trained and tested using the available experimental data. The coefficients of determination of ANN and regression models indicate that there is a strong correlation between the measured and predicted yarn characteristics with an acceptable mean absolute error values. The comparative analysis of two modelling techniques shows that the ANNs perform better than the MLR models. The relative importance of input variables was determined using rank analysis through input saliency test on optimised ANN models and standardised coefficients of regression models. These models are suitable for yarn manufacturers and can be used within the investigated knowledge domain.

Artificial Neural Networks Estimation for Thicknesses of Multilayer Nano-Scale Films

2019 ◽  
Vol 962 ◽  
pp. 41-48
Author(s):  
Tzong Daw Wu ◽  
Jiun Shen Chen ◽  
Ching Pei Tseng ◽  
Cheng Chang Hsieh
Keyword(s):  
Thin Films ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real Time ◽  
Film Growth ◽  
Mean Squared Error ◽  
Squared Error ◽  
Thin Film Optics ◽  
The Mean ◽  
Artificial Neural

This study presents a real-time method for determining the thickness of each layer in multilayer thin films. Artificial neural networks (ANNs) were introduced to estimate thicknesses from a transmittance spectrum. After training via theoretical spectra which were generated by thin-film optics and modified by noise, ANNs were applied to estimate the thicknesses of four-layer nanoscale films which were TiO2, Ag, Ti, and TiO2 thin films assembled sequentially on polyethylene terephthalate (PET) substrates. The results reveal that the mean squared error of the estimation is 2.6 nm2, and is accurate enough to monitor film growth in real time.

The Mean Angular Distance Among Objects and Its Relationships with Kohonen Artificial Neural Networks

2003 ◽  
Vol 43 (5) ◽  
pp. 1403-1411 ◽  
Author(s):  
Jorge F. Magallanes ◽  
Jure Zupan ◽  
Darío Gomez ◽  
Silvia Reich ◽  
Laura Dawidowski ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Angular Distance ◽  
The Mean ◽  
Artificial Neural

scholarly journals DESENVOLVIMENTO DE REDES NEURAIS ARTIFICIAIS PARA ESTIMATIVA DAS VAZÕES DIÁRIAS NA BACIA DO RIO PIRACICABA5

Irriga ◽  
2018 ◽  
Vol 23 (4) ◽  
pp. 756-772
Author(s):  
Eduardo Morgan Uliana ◽  
Demetrius David da Silva ◽  
Michel Castro Moreira ◽  
Donizete Dos Reis Pereira ◽  
Silvio Bueno Pereira ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
River Basin ◽  
Hydrologic Modeling ◽  
Minas Gerais ◽  
Mato Grosso ◽  
Artificial Neural ◽  
E Mail ◽  
Daily Flows ◽  
Piracicaba River

DESENVOLVIMENTO DE REDES NEURAIS ARTIFICIAIS PARA ESTIMATIVA DAS VAZÕES DIÁRIAS NA BACIA DO RIO PIRACICABA5     EDUARDO MORGAN ULIANA1; DEMETRIUS DAVID DA SILVA2; MICHEL CASTRO MOREIRA3; DONIZETE DOS REIS PEREIRA4; SILVIO BUENO PEREIRA2 E FREDERICO TERRA DE ALMEIDA1   1Universidade Federal de Mato Grosso (UFMT), Instituto de Ciências Agrárias e Ambientais (ICAA), Avenida Alexandre Ferronato, CEP.: 78557-267, Sinop – MT, Brasil, e-mail: [email protected], [email protected]. 2Universidade Federal de Viçosa (UFV), Departamento de Engenharia Agrícola, Avenida Peter Henry Rolfs, CEP.: 36570-900, Viçosa – MG, Brasil, e-mail: [email protected]. 3Universidade Federal do Oeste da Bahia, Centro das Ciências Exatas e das Tecnologias, Rua Professor José Seabra de Lemos, CEP.: 47808-021, Barreiras – BA, Brasil, e-mail: [email protected]. 4Universidade Federal de Viçosa (UFV), Instituto de Ciências Agrárias, Rodovia LMG 818, km 06, Florestal – MG, Brasil, e-mail: [email protected]. 5O artigo é referente ao capítulo 3 da tese de doutorado do primeiro autor.     1 RESUMO   As Redes Neurais Artificiais (RNAs) são uma alternativa na modelagem hidrológica para a estimativa das vazões dos cursos de água a partir de dados hidrometeorológicos. O objetivo do trabalho foi desenvolver Redes Neurais Artificiais para estimar as vazões diárias na bacia hidrográfica do rio Piracicaba, Minas Gerais. O estudo foi realizado em três seções de monitoramento de vazão da bacia do rio Piracicaba, localizada no Estado de Minas Gerais - Brasil. No desenvolvimento das RNAs foram realizados a coleta e seleção dos dados; a definição da arquitetura da rede; e o treinamento e validação das redes desenvolvidas. A maior parte das RNAs desenvolvidas apresentou coeficiente de Nash-Sutcliffe maior que 0,80 o que permitiu classificar os modelos como bons para a estimativa das vazões. Com base nos resultados, pode-se concluir que as RNAs são adequadas para a estimativa das vazões diárias na bacia do rio Piracicaba e podem ser empregadas na estimativa de eventos extremos e no gerenciamento dos recursos hídricos.   Palavras-Chave: modelo empírico inteligência artificial, modelagem hidrológica.     ULIANA, E. M.; SILVA, D. D.; MOREIRA, M. C.; PEREIRA, D. R.; PEREIRA, S. B.; ALMEIDA, F. T. ARTIFICIAL NEURAL NETWORKS FOR DAILY FLOW ESTIMATES IN THE PIRACICABA RIVER BASIN     2 ABSTRACT   Artificial neural networks (ANNs) have been used alternatively in hydrologic modeling to estimate accurately watercourse flows based on hydrometeorological data. This study developed artificial neural networks to estimate daily flows in Piracicaba river basin, in Minas Gerais state (Brazil). For this, we used three runoff-monitoring sections of the Piracicaba river basin, with an area of 5,304.0 km2, and located in the State of Minas Gerais – Brazil. For designing the ANNs to estimate daily flows, we adopted the following steps: data collection and selection, network architecture definition, training and validation of results. The results showed that ANNs are adequate to estimate daily flows in Piracicaba river basin.   Keywords: empirical model, artificial intelligence, hydrologic modeling.

scholarly journals Comparison of Methods for Harvest Prediction in ‘Gigante’ Cactus Pear

2019 ◽  
Vol 11 (14) ◽  
pp. 216 ◽  
Author(s):  
Bruno V. C. Guimarães ◽  
Sérgio L. R. Donato ◽  
Ignacio Aspiazú ◽  
Alcinei M. Azevedo ◽  
Abner J. de Carvalho
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Regression Models ◽  
Coefficient Of Determination ◽  
Cactus Pear ◽  
Federal Institute ◽  
The Mean ◽  
Artificial Neural

Behavior analysis and plant expression are the answers the researcher needs to construct predictive models that minimize the effects of the uncertainties of field production. The objective of this study was to compare the simple and multiple linear regression methods and the artificial neural networks to allow the maximum security in the prediction of harvest in ‘Gigante’ cactus pear. The uniformity test was conducted at the Federal Institute of Bahia, Campus Guanambi, Bahia, Brazil, coordinates 14°13′30″ S, 42°46′53″ W and altitude of 525 m. At 930 days after planting, we evaluated 384 basic units, in which were measured the following variables: plant height (PH); cladode length (CL), width (CW) and thickness (CT); cladode number (CN); total cladode area (TCA); cladode area (CA) and cladode yield (Y). For the comparison between the artificial neural networks (ANN) and regression models (single and multiple-SLR and MLR), we considered the mean prediction error (MPE), the mean quadratic error (MQE), the mean square of deviation (MSD) and the coefficient of determination (R2).The values estimated by the ANN 7-5-1 showed the best proximity to the data obtained in field conditions, followed by ANN 6-2-1, MLR (TCA and CT), SLR (TCA) and SLR (CN). In this way, the ANN models with the topologies 7-2-1 and 6-2-1, MLR with the variables total cladode area and cladode thickness and SLR with the isolated descriptors total cladode area and cladode number, explain 85.1; 81.5; 76.3; 74.09 and 65.87%, respectively, of the yield variation. The ANNs were more efficient at predicting the yield of the ‘Gigante’ cactus pear when compared to the simple and multiple linear regression models.

scholarly journals Prediction by using Artificial Neural Networks and Box-Jenkins methodologies: Comparison Study

2017 ◽  
Vol 9 (2) ◽  
Author(s):  
Mohammed Habib Al- Sharoot ◽  
Emaan Yousif Abdoon
Keyword(s):  
Neural Networks ◽  
Exchange Rate ◽  
Absolute Error ◽  
Percentage Error ◽  
Neural Network Approach ◽  
Decision Making Processes ◽  
The Mean ◽  
Us Dollar ◽  
Stable Economy ◽  
Better Than

The variations in exchange rate, especially the sudden unexpected increases and decreases, have significant impact on the national economy of any country. Iraq is no exception; therefore, the accurate forecasting of exchange rate of Iraqi dinar to US dollar plays an important role in the planning and decision-making processes as well as the maintenance of a stable economy in Iraq. This research aims to compare Box-Jenkins methodology to neural networks in terms of forecasting the exchange rate of Iraqi dinar to US dollar based on data provided by the Iraqi Central Bank for the period  30/01/2004 and 30/12/2014. Based on the Mean Square Error (MSE), the Mean Absolute Error (MAE), and the Mean Absolute Percentage Error (MAPE) as criteria to compare the two methodologies, it was concluded that Box-Jenkins is better than neural network approach in forecasting.

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