Application of Artificial Neural Models for Planning Sport Training in 110m Hurdles

2017 ◽  
Author(s):  
Krzysztof Przednowek ◽  
Janusz Iskra ◽  
Tomasz Krzeszowski ◽  
Karolina H. Przednowek
Keyword(s):  
Neural Models ◽  
Sport Training ◽  
Artificial Neural

scholarly journals Modeling of milk lactose removal by column adsorption using artificial neural networks: MLP and RBF

2019 ◽  
Vol 25 (4) ◽  
pp. 369-382
Author(s):  
Manuela Leite ◽  
Matheus Santos ◽  
Eulina Costa ◽  
Acenini Balieiro ◽  
Álvaro Lima ◽  
...  
Keyword(s):  
Adsorption Process ◽  
Radial Basis Function Network ◽  
Fixed Bed ◽  
Computational Time ◽  
Multilayer Perceptrons ◽  
Neural Models ◽  
Fixed Bed Column ◽  
Column Adsorption ◽  
Ann Models ◽  
Artificial Neural

Artificial neural network (ANN) techniques are effective in modeling nonlinear processes, are simple to implement and require low computational time. In this work, the lactose adsorption process for continuous flow in a fixed-bed column with a molecularly imprinted polymer (MIP) adsorbent was modeled using an ANN technique. The neural models allowed predicting the relative lactose concentration (C/C0) from the interactions between the variables of contact time (min), temperature (?C), granulometry (mesh), bed height (cm) and flow rate (mL min-1). The ANN models were developed in MATLAB using multilayer perceptrons (MLP) and a radial basis function network (RBF). The MLP model was developed using a three-layer feed forward backpropagation network with 5, 8 and 4 neurons in the first, second and third layer, respectively. The function (RBF) network is also proposed and its performance is compared to a traditional network type. The best architecture configuration RBF model was developed using 5, 14 and 1 neurons in the first, second and third layer, respectively. The proposal of development of mathematical models applied to multi-component adsorption system for milk using these approaches is innovative. The resulting breakthrough curve models for lactose adsorption were in good agreement with the experimental results. Performance indices, such as R?, MSE, RMSE, SSE, MAE and RME were used to evaluate the reliabilities and accuracies of the models. A comparison between the ANN models shows the ability to predict the breakthrough curves of lactose removal in the milk adsorption process. Though, the MLP network model shows more accurately a higher correlation coefficient (R2 = 0.9751) and lower values for the obtained error indices. The accuracy of the model is confirmed by the comparison between the predicted and experimental data. The results showed that both neural models efficiently described the non-linear process of lactose adsorption in a fixed-bed column.

scholarly journals Hydrodynamic object identification with artificial neural models

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Sreetej Lakkam ◽  
B. T. Balamurali ◽  
Roland Bouffanais
Keyword(s):  
Object Identification ◽  
Neural Models ◽  
Artificial Neural

scholarly journals Application of Artificial Neural Networks to Analyze the Concentration of Ferulic Acid, Deoxynivalenol, and Nivalenol in Winter Wheat Grain

Agriculture ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 127 ◽  
Author(s):  
Gniewko Niedbała ◽  
Danuta Kurasiak-Popowska ◽  
Kinga Stuper-Szablewska ◽  
Jerzy Nawracała
Keyword(s):  
Neural Network ◽  
Winter Wheat ◽  
Ferulic Acid ◽  
Wheat Variety ◽  
Wheat Yield ◽  
Neural Models ◽  
Pilot Studies ◽  
Chemical Protection ◽  
Artificial Neural ◽  
The Impact

Biotic stress, which includes infection by pathogenic fungi, causes losses of wheat yield in terms of quantity and quality. Ear Fusarium is caused by strains of F. graminearum and F. culmorum, which can produce mycotoxins—deoxynivalenol (DON) and nivalenol (NIV). One of the wheat’s defense mechanisms against stressors is the activation of biosynthesis pathways of antioxidant compounds, including ferulic acid. The aim of the study was to conduct pilot studies on the basis of which neural models were created that would examine the impact of the variety and weather conditions on the concentration of ferulic acid, and link its content with the concentration of deoxynivalenol and nivalenol. The plant material was 23 winter wheat genotypes with different Fusarium resistance. The field experiment was conducted in 2011–2013 in Poland in three experimental combinations, namely: with full chemical protection; without chemical protection, but infested with natural disease (control); and in the absence of fungicidal protection, with artificial inoculation by genus Fusarium fungi. As a result of the pilot studies, three neural models—FERUANN analytical models (ferulic acid content), DONANN (deoxynivalenol content) and NIVANN (nivalenol content)—were produced. Each model was based on 14 independent features, 12 of which were in the form of quantitative data, and the other two were presented as qualitative data. The structure of the created models was based on an artificial neural network (ANN) of the multilayer perceptron (MLP) with two hidden layers. The sensitivity analysis of the neural network showed the two most important features determining the concentration of ferulic acid, deoxynivalenol, and nivalenol in winter wheat seeds. These are the experiment variant (VAR) and winter wheat variety (VOW).

scholarly journals Assessment of Arias Intensity of historical earthquakes using modified Mercalli intensities and artificial neural networks

2011 ◽  
Vol 11 (12) ◽  
pp. 3097-3105 ◽  
Author(s):  
G-A. Tselentis
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Historical Earthquakes ◽  
Forecast Model ◽  
Seismic Intensity ◽  
Nonlinear Behaviour ◽  
Neural Models ◽  
Arias Intensity ◽  
Data Synthesis ◽  
Artificial Neural

Abstract. This paper presents the development of a non-parametric forecast model based on artificial neural networks for the direct assessment of Arias Intensity corresponding to a historic earthquake using seismic intensity data. The neural models allow complex and nonlinear behaviour to be tracked. Application of this methodology on earthquakes with known instrumental data from Greece, showed that the artificial neural network forecast model have excellent data synthesis capability.

scholarly journals Planning Training Loads for The 400 M Hurdles in Three-Month Mesocycles Using Artificial Neural Networks

2017 ◽  
Vol 60 (1) ◽  
pp. 175-189 ◽  
Author(s):  
Krzysztof Przednowek ◽  
Janusz Iskra ◽  
Krzysztof Wiktorowicz ◽  
Tomasz Krzeszowski ◽  
Adam Maszczyk
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Mean Squared Error ◽  
Radial Basis Function Network ◽  
Training Data ◽  
Neural Models ◽  
National Team ◽  
Novel Approach ◽  
Radial Basis ◽  
Artificial Neural

Abstract This paper presents a novel approach to planning training loads in hurdling using artificial neural networks. The neural models performed the task of generating loads for athletes’ training for the 400 meters hurdles. All the models were calculated based on the training data of 21 Polish National Team hurdlers, aged 22.25 ± 1.96, competing between 1989 and 2012. The analysis included 144 training plans that represented different stages in the annual training cycle. The main contribution of this paper is to develop neural models for planning training loads for the entire career of a typical hurdler. In the models, 29 variables were used, where four characterized the runner and 25 described the training process. Two artificial neural networks were used: a multi-layer perceptron and a network with radial basis functions. To assess the quality of the models, the leave-one-out cross-validation method was used in which the Normalized Root Mean Squared Error was calculated. The analysis shows that the method generating the smallest error was the radial basis function network with nine neurons in the hidden layer. Most of the calculated training loads demonstrated a non-linear relationship across the entire competitive period. The resulting model can be used as a tool to assist a coach in planning training loads during a selected training period.

scholarly journals Estimation of Coal’s Sorption Parameters Using Artificial Neural Networks

Materials ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 5422
Author(s):  
Marta Skiba ◽  
Mariusz Młynarczuk
Keyword(s):  
Diffusion Coefficient ◽  
Sorption Capacity ◽  
Effective Diffusion Coefficient ◽  
Effective Diffusion ◽  
Gravimetric Method ◽  
Neural Models ◽  
Petrographic Composition ◽  
Artificial Neural ◽  
Sorption Parameters ◽  
Maximal Sorption Capacity

This article presents research results into the application of an artificial neural network (ANN) to determine coal’s sorption parameters, such as the maximal sorption capacity and effective diffusion coefficient. Determining these parameters is currently time-consuming, and requires specialized and expensive equipment. The work was conducted with the use of feed-forward back-propagation networks (FNNs); it was aimed at estimating the values of the aforementioned parameters from information obtained through technical and densitometric analyses, as well as knowledge of the petrographic composition of the examined coal samples. Analyses showed significant compatibility between the values of the analyzed sorption parameters obtained with regressive neural models and the values of parameters determined with the gravimetric method using a sorption analyzer (prediction error for the best match was 6.1% and 0.2% for the effective diffusion coefficient and maximal sorption capacity, respectively). The established determination coefficients (0.982, 0.999) and the values of standard deviation ratios (below 0.1 in each case) confirmed very high prediction capacities of the adopted neural models. The research showed the great potential of the proposed method to describe the sorption properties of coal as a material that is a natural sorbent for methane and carbon dioxide.

scholarly journals Optimization of Artificial Neural Networks Based Models for Wave Height Prediction

2020 ◽  
Vol 173 ◽  
pp. 03007
Author(s):  
Gheorghe Stăvărache ◽  
Sorin Ciortan ◽  
Eugen Rusu
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wave Height ◽  
Neural Model ◽  
Neural Models ◽  
Mathematical Formulas ◽  
Height Prediction ◽  
Energy Conversion System ◽  
Artificial Neural ◽  
And Training

For an efficient wave energy extraction, the evolution of some specific parameters must be known. These parameters, like significant wave height and period, are mainly determined by the wind speed and influenced by some sea environment characteristics. Their evolution in time is one of the basic information necessary for designing of an accurate energy conversion system. In many scientific works the benefits of artificial neural networks based modeling are presented. These models allow the prediction and optimization of the wave parameters starting from experimentally acquired data. Due to specific calculus method of the artificial neural networks, in order to obtain accurate results, a very important step is the appropriate neural model design. If the model is optimal correlated with the data processed, the results obtained can be more significant than those coming from the mathematical formulas. The main neural models parameters that must be taken into account for an optimal design are model structure, transfer function and training algorithm. This paper presents an investigation of the results obtained with different models, proving that for a specific dataset a specific neural model offers the best results. Several models are analyzed, for a dataset corresponding to specific point in Black Sea and a comparison of results is presented.

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