scholarly journals Redes Neuronales Artificiales: factores que determinan la cosecha de caña en la industria azucarera.// Artificial Neural Networks: factors that determine the cane harvest in a sugar industry.

Ciencia Unemi ◽  
2019 ◽  
Vol 12 (29) ◽  
pp. 36-50
Author(s):  
Italo Mendoza-Haro ◽  
Hiram Marquetti-Nodarse
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Sugar Industry ◽  
Expert Assessment ◽  
Mathematical Tool ◽  
Development Environment ◽  
Thermal Oscillation ◽  
Historical Database ◽  
Artificial Neural ◽  
The City

La investigación muestra lo importante de las redes neuronales artificiales dentro de la industria azucarera, como una herramienta útil para la predicción del cultivo de la caña de azúcar, tomando como entradas la información climatológica: temperaturas máximas y mínimas, oscilación térmica, precipitaciones, heliofanía, humedad relativa, evaporación y hectáreas de los cultivos sembrados, para obtener una salida: toneladas de caña. Se desarrolló una herramienta de trabajo predictiva con resultados confiables, comparados con métodos tradicionales utilizados, como los aforos de expertos para la cosecha de la caña de azúcar. Se analizó la base de datos histórica de la organización, mediante un software MATLAB, herramienta matemática, que ofrece un entorno de desarrollo integrado (IDE) con lenguaje M de programación propio. La investigación se desarrolló en Compañía Azucarera Valdez S.A. Ubicada en la Ciudad de Milagro-Provincia del Guayas-Ecuador.AbstractThe research shows the importance of artificial neural networks within the sugar industry, as a useful tool for the prediction of the cultivation of sugarcane, taking as input the climatological information: maximum and minimum temperatures, thermal oscillation, rainfall, heliophany, relative humidity, evaporation and hectares of crops planted, to obtain tons of cane as an output. A predictive work tool with reliable results was developed, compared with traditional methods used, such as expert assessment for sugarcane harvesting. The historical database of the organization was analyzed through MATLAB software, a mathematical tool which offers an integrated development environment (IDE) with its own M programming language. The research was developed at Compañía Azucarera Valdez S.A. located in the City of Milagro-Province of Guayas-Ecuador.

Predictive performance modeling of Habesha brewery wastewater treatment plant using artificial neural networks

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Wastewater Treatment ◽  
Artificial Neural Networks ◽  
Performance Modeling ◽  
Predictive Performance ◽  
Mathematical Tool ◽  
Effluent Water ◽  
Artificial Neural

Recently, process control in wastewater treatment plants (WWTPs) is, mostly accomplished through examining the quality of the water effluent and adjusting the processes through the operator’s experience. This practice is inefficient, costly and slow in control response. A better control of WTPs can be achieved by developing a robust mathematical tool for performance prediction. Due to their high accuracy and quite promising application in the field of engineering, Artificial Neural Networks (ANNs) are attracting attention in the domain of WWTP predictive performance modeling. This work focuses on applying ANN with a feed-forward, back propagation learning paradigm to predict the effluent water quality of the Habesha brewery WTP. Data of influent and effluent water quality covering approximately an 11-month period (May 2016 to March 2017) were used to develop, calibrate and validate the models. The study proves that ANN can predict the effluent water quality parameters with a correlation coefficient (R) between the observed and predicted output values reaching up to 0.969. Model architecture of 3-21-3 for pH and TN, and 1-76-1 for COD were selected as optimum topologies for predicting the Habesha Brewery WTP performance. The linear correlation between predicted and target outputs for the optimal model architectures described above were 0.9201 and 0.9692, respectively.

PREDIKSI PERTUMBUHAN PENDUDUK PADA PROVINSI SUMATERA BARAT DENGAN MENGGUNAKAN METODE BACKPROPAGATION

2020 ◽  
Vol 7 (4) ◽  
pp. 216
Author(s):  
Hijrah Yanti Sitanggang ◽  
Vera Irma Delianti
Keyword(s):  
Neural Networks ◽  
Growth Rate ◽  
Artificial Neural Networks ◽  
Population Growth ◽  
Population Growth Rate ◽  
Backpropagation Neural Networks ◽  
West Sumatra ◽  
Artificial Neural ◽  
The City

The problem of population is one of the problems in the Province of West Sumatra, especially in the City of Padang, Kota Bukitinggi, and the City of Payakumbuh which has a very fast population growth rate, this occurs due to several factors such as births, deaths, residents who come, and residents who leave. The highest population growth occurred in Padang City in 2018 amounting to 939,112 residents and the smallest population growth occurred in the City of Bukitinggi in 2014 amounting to 120,491 residents. The purpose of this study is to predict population growth that will occur in 2019 in the cities of Padang, Bukittinggi and Payakumbuh. The method used in this research is descriptive correlational by applying backpropagation neural networks. The application used is Matlab. Based on the problems and methods obtained, the predicted results in 2019 in Padang City amounted to 124,7150, Bukittinggi numbered 126,8040 and Payakumbuh totaled 128.7830.  Keywords: Artificial Neural Networks, Backpropagation, Matlab.

scholarly journals Comparison of neural networks and genetic algorithms to determine missing precipitation data (Case study: the city of Sari)

2020 ◽  
Vol 11 (29) ◽  
pp. 114-128
Author(s):  
Ali Mahdavi ◽  
Mohsen Najarchi ◽  
Emadoddin Hazaveie ◽  
Seyed Mohammad Mirhosayni Hazave ◽  
Seyed Mohammad Mahdai Najafizadeh
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Genetic Programming ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Correlation Coefficients ◽  
Mean Square ◽  
Artificial Neural ◽  
The City

Neural networks and genetic programming in the investigation of new methods for predicting rainfall in the catchment area of the city of Sari. Various methods are used for prediction, such as the time series model, artificial neural networks, fuzzy logic, fuzzy Nero, and genetic programming. Results based on statistical indicators of root mean square error and correlation coefficient were studied. The results of the optimal model of genetic programming were compared, the correlation coefficients and the root mean square error 0.973 and 0.034 respectively for training, and 0.964 and 0.057 respectively for the optimal neural network model. Genetic programming has been more accurate than artificial neural networks and is recommended as a good way to accurately predict.

The accurate estimation of physicochemical properties of ternary mixtures containing ionic liquids via artificial neural networks

2015 ◽  
Vol 17 (6) ◽  
pp. 4533-4537 ◽  
Author(s):  
John C. Cancilla ◽  
Pablo Díaz-Rodríguez ◽  
Gemma Matute ◽  
José S. Torrecilla
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Ionic Liquids ◽  
Physicochemical Properties ◽  
Ternary Mixture ◽  
Ternary Mixtures ◽  
Accurate Estimation ◽  
Mathematical Tool ◽  
Artificial Neural

A graphic scheme of the mathematical tool designed is able to estimate physicochemical properties of a ternary mixture.

scholarly journals Real-time estimation of free spaces in regulated on-street parking spaces using artificial neural networks

2016 ◽  
Author(s):  
Miguel Magaña Suarez
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real Time ◽  
Time Estimation ◽  
Mobile App ◽  
Saturation State ◽  
Real Time Estimation ◽  
Artificial Neural ◽  
Free Spaces ◽  
The City

In this paper we will develop a methodology for estimating the percentage of free parking spaces available in the area of the city where a user is interested through a real-time query in a mobile app. The smartphone screen will provide a colour-coded map of the requested area that indicates the saturation state of the parking spaces.DOI: http://dx.doi.org/10.4995/CIT2016.2016.3528

scholarly journals Classification of Images Acquired with Colposcopy Using Artificial Neural Networks

2014 ◽  
Vol 13 ◽  
pp. CIN.S17948 ◽  
Author(s):  
Priscyla W. Simões ◽  
Narjara B. Izumi ◽  
Ramon S. Casagrande ◽  
Ramon Venson ◽  
Carlos D. Veronezi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Analytical Study ◽  
Self Organizing Maps ◽  
Hybrid Neural Network ◽  
Artificial Neural ◽  
Validation Set ◽  
Classification Of Images ◽  
The City

Objective To explore the advantages of using artificial neural networks (ANNs) to recognize patterns in colposcopy to classify images in colposcopy. PURPOSE: Transversal, descriptive, and analytical study of a quantitative approach with an emphasis on diagnosis. The training test e validation set was composed of images collected from patients who underwent colposcopy. These images were provided by a gynecology clinic located in the city of Criciúma (Brazil). The image database ( n = 170) was divided; 48 images were used for the training process, 58 images were used for the tests, and 64 images were used for the validation. A hybrid neural network based on Kohonen self-organizing maps and multilayer perceptron (MLP) networks was used. Results After 126 cycles, the validation was performed. The best results reached an accuracy of 72.15%, a sensibility of 69.78%, and a specificity of 68%. Conclusion Although the preliminary results still exhibit an average efficiency, the present approach is an innovative and promising technique that should be deeply explored in the context of the present study.

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