Advanced approach to numerical forecasting using artificial neural networks

Current global market is driven by many factors, such as the information age, the time and amount of information distributed by many data channels it is practically impossible analyze all kinds of incoming information flows and transform them to data with classical methods. New requirements could be met by using other methods. Once trained on patterns artificial neural networks can be used for forecasting and they are able to work with extremely big data sets in reasonable time. The patterns used for learning process are samples of past data. This paper uses Radial Basis Functions neural network in comparison with Multi Layer Perceptron network with Back-propagation learning algorithm on prediction task. The task works with simplified numerical time series and includes forty observations with prediction for next five observations. The main topic of the article is the identification of the main differences between used neural networks architectures together with numerical forecasting. Detected differences then verify on practical comparative example.

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Prediction of adsorption efficiencies of Ni (II) in aqueous solutions with perlite via artificial neural networks

Archives of Environmental Protection ◽

10.1515/aep-2017-0034 ◽

2017 ◽

Vol 43 (4) ◽

pp. 26-32 ◽

Cited By ~ 2

Author(s):

Sinan Mehmet Turp

Keyword(s):

Neural Network ◽

Experimental Data ◽

Neural Networks ◽

Artificial Neural Network ◽

Artificial Neural Networks ◽

Back Propagation ◽

Data Sets ◽

Adsorption Efficiency ◽

Back Propagation Algorithm ◽

Artificial Neural

AbstractThis study investigates the estimated adsorption efficiency of artificial Nickel (II) ions with perlite in an aqueous solution using artificial neural networks, based on 140 experimental data sets. Prediction using artificial neural networks is performed by enhancing the adsorption efficiency with the use of Nickel (II) ions, with the initial concentrations ranging from 0.1 mg/L to 10 mg/L, the adsorbent dosage ranging from 0.1 mg to 2 mg, and the varying time of effect ranging from 5 to 30 mins. This study presents an artificial neural network that predicts the adsorption efficiency of Nickel (II) ions with perlite. The best algorithm is determined as a quasi-Newton back-propagation algorithm. The performance of the artificial neural network is determined by coefficient determination (R2), and its architecture is 3-12-1. The prediction shows that there is an outstanding relationship between the experimental data and the predicted values.

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A new approach to training back-propagation artificial neural networks: empirical evaluation on ten data sets from clinical studies

Statistics in Medicine ◽

10.1002/sim.1107 ◽

2002 ◽

Vol 21 (9) ◽

pp. 1309-1330 ◽

Cited By ~ 16

Author(s):

Antonio Ciampi ◽

Fulin Zhang

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Clinical Studies ◽

Empirical Evaluation ◽

Back Propagation ◽

Data Sets ◽

New Approach ◽

Artificial Neural

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Application of Artificial Neural Networks for estimating index floods

Contributions to Geophysics and Geodesy ◽

10.2478/v10126-012-0014-7 ◽

2012 ◽

Vol 42 (4) ◽

pp. 295-311 ◽

Cited By ~ 2

Author(s):

Viliam Šimor ◽

Kamila Hlavčová ◽

Silvia Kohnová ◽

Ján Szolgay

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Multiple Regression ◽

Regression Models ◽

Learning Algorithm ◽

Back Propagation ◽

Activation Function ◽

Multiple Regression Models ◽

Artificial Neural ◽

Hidden Layer

Abstract This article presents an application of Artificial Neural Networks (ANNs) and multiple regression models for estimating mean annual maximum discharge (index flood) at ungauged sites. Both approaches were tested for 145 small basins in Slovakia in areas ranging from 20 to 300 km2. Using the objective clustering method, the catchments were divided into ten homogeneous pooling groups; for each pooling group, mutually independent predictors (catchment characteristics) were selected for both models. The neural network was applied as a simple multilayer perceptron with one hidden layer and with a back propagation learning algorithm. Hyperbolic tangents were used as an activation function in the hidden layer. Estimating index floods by the multiple regression models were based on deriving relationships between the index floods and catchment predictors. The efficiencies of both approaches were tested by the Nash-Sutcliffe and a correlation coefficients. The results showed the comparative applicability of both models with slightly better results for the index floods achieved using the ANNs methodology.

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Artificial neural networks employment in the prediction of evapotranspiration of greenhouse-grown sweet pepper

Revista Brasileira de Engenharia Agrícola e Ambiental ◽

10.1590/1807-1929/agriambi.v20n6p507-512 ◽

2016 ◽

Vol 20 (6) ◽

pp. 507-512 ◽

Cited By ~ 5

Author(s):

Héliton Pandorfi ◽

Alan C. Bezerra ◽

Roberto T. Atarassi ◽

Frederico M. C. Vieira ◽

José A. D. Barbosa Filho ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Network Architecture ◽

Learning Algorithm ◽

Meteorological Data ◽

Back Propagation ◽

Sweet Pepper ◽

Coefficient Of Determination ◽

Error Back Propagation ◽

Artificial Neural

ABSTRACT This study aimed to investigate the applicability of artificial neural networks (ANNs) in the prediction of evapotranspiration of sweet pepper cultivated in a greenhouse. The used data encompass the second crop cycle, from September 2013 to February 2014, constituting 135 days of daily meteorological data, referring to the following variables: temperature and relative air humidity, wind speed and solar radiation (input variables), as well as evapotranspiration (output variable), determined using data obtained by load-cell weighing lysimeter. The recorded data were divided into three sets for training, testing and validation. The ANN learning model recognized the evapotranspiration patterns with acceptable accuracy, with mean square error of 0.005, in comparison to the data recorded in the lysimeter, with coefficient of determination of 0.87, demonstrating the best approximation for the 4-21-1 network architecture, with multilayers, error back-propagation learning algorithm and learning rate of 0.01.

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ARTIFICIAL NEURAL NETWORKS IN THE IDENTIFICATION OF PERIPHERAL NERVE DISORDERS

Journal of Mechanics in Medicine and Biology ◽

10.1142/s0219519412400180 ◽

2012 ◽

Vol 12 (04) ◽

pp. 1240018 ◽

Cited By ~ 2

Author(s):

REEDA KUNHIMANGALAM ◽

SUJITH OVALLATH ◽

PAUL K. JOSEPH

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Peripheral Nerve ◽

Confusion Matrix ◽

Back Propagation ◽

Data Sets ◽

Training Algorithms ◽

Feed Forward Neural Network ◽

Medical College ◽

Artificial Neural

The recent years have witnessed an increase in the use of newer analytical tools in the field of medicine to assist in diagnostic procedure. Among the new tools, artificial neural networks (ANNs) have received particular attention because of their ability to analyze complex nonlinear data sets. This study suggests that ANNs can be used for the diagnosis of peripheral nerve disorders particularly the carpal tunnel syndrome (CTS) and neuropathy. This paper aims at building a classifier using a feed forward neural network that can distinguish between CTS, neuropathy, and normal controls using a reduced set of measurements or features from nerve conduction study (NCS) data. Three different ANN training algorithms, viz. Levenberg–Marquardt (LM), Conjugate gradient (CGB), and resilient back-propagation (RP) are used to see which algorithm produces better results and has faster training for the application under consideration. The data used were obtained from the Neurology Department, Kannur Medical College, Kerala, India. The obtained resultant confusion matrix indicated only a few misclassifications in all the three cases. The analysis showed that the CGB and RB algorithms provide faster convergence on pattern recognition problems, but the best performance in terms of accuracy is given by the LM algorithm. The accuracy obtained for the LM, CGB, and RB were 98.3%, 97.8%, and 97.2%, respectively. The respective sensitivities were 96.1%, 94.1%, and 94.1%, while the specificities were found to be equal to 99.4%, 98.8%, and 97.5%, respectively. The study aims at showing that ANNs may prove useful in combination with other systems in providing diagnostic and predictive medical opinions. However, it must always be kept in mind that ANNs represent only one form of computer-aided diagnosis, and the clinician's responsibility and overall control of patient care should never be underestimated.

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New methods based on back propagation (BP) and radial basis function (RBF) artificial neural networks (ANNs) for predicting the occurrence of haloketones in tap water

The Science of The Total Environment ◽

10.1016/j.scitotenv.2021.145534 ◽

2021 ◽

Vol 772 ◽

pp. 145534 ◽

Cited By ~ 2

Author(s):

Ying Deng ◽

Xiaoling Zhou ◽

Jiao Shen ◽

Ge Xiao ◽

Huachang Hong ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Radial Basis Function ◽

Basis Function ◽

Tap Water ◽

Back Propagation ◽

New Methods ◽

Radial Basis ◽

Artificial Neural

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Prediction of Ferrite-Martensite Dual-Phase Steels Mechanical Properties by Use of Artificial Neural Networks

Materials Science Forum ◽

10.4028/www.scientific.net/msf.773-774.268 ◽

2013 ◽

Vol 773-774 ◽

pp. 268-274

Author(s):

Amir Ghiami ◽

Ramin Khamedi

Keyword(s):

Mechanical Properties ◽

Neural Networks ◽

Artificial Neural Networks ◽

Back Propagation ◽

Total Elongation ◽

Ultimate Tensile Stress ◽

Dual Phase ◽

Dual Phase Steels ◽

Good Ability ◽

Artificial Neural

This paper presents an investigation of the capabilities of artificial neural networks (ANN) in predicting some mechanical properties of Ferrite-Martensite dual-phase steels applicable for different industries like auto-making. Using ANNs instead of different destructive and non-destructive tests to determine the material properties, reduces costs and reduces the need for special testing facilities. Networks were trained with use of a back propagation (BP) error algorithm. In order to provide data for training the ANNs, mechanical properties, inter-critical annealing temperature and information about the microstructures of many specimens were examined. After the ANNs were trained, the four parameters of yield stress, ultimate tensile stress, total elongation and the work hardening exponent were simulated. Finally a comparison of the predicted and experimental values indicates that the results obtained from the given input data reveal a good ability of the well-trained ANN to predict the described mechanical properties.

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Rainfall Prediction by Artificial Neural Networks Trained using Different Climate Variables

10.21203/rs.3.rs-569733/v1 ◽

2021 ◽

Author(s):

Mateus Alexandre da Silva ◽

Marina Neves Merlo ◽

Michael Silveira Thebaldi ◽

Danton Diego Ferreira ◽

Felipe Schwerz ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Input Data ◽

Back Propagation ◽

Metropolitan Region ◽

Climate Variables ◽

Back Propagation Algorithm ◽

Rainfall Prediction ◽

Artificial Neural ◽

Belo Horizonte

Abstract Predicting rainfall can prevent and mitigate damages caused by its deficit or excess, besides providing necessary tools for adequate planning for the use of water. This research aimed to predict the monthly rainfall, one month in advance, in four municipalities in the metropolitan region of Belo Horizonte, using artificial neural networks (ANN) trained with different climate variables, and to indicate the suitability of such variables as inputs to these models. The models were developed through the MATLAB® software version R2011a, using the NNTOOL toolbox. The ANN’s were trained by the multilayer perceptron architecture and the Feedforward and Back propagation algorithm, using two combinations of input data were used, with 2 and 6 variables, and one combination of input data with 3 of the 6 variables most correlated to observed rainfall from 1970 to 1999, to predict the rainfall from 2000 to 2009. The most correlated variables to the rainfall of the following month are the sequential number corresponding to the month, total rainfall and average compensated temperature, and the best performance was obtained with these variables. Furthermore, it was concluded that the performance of the models was satisfactory; however, they presented limitations for predicting months with high rainfall.

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