scholarly journals Modelling and Predicting Backstroke Start Performance Using Non-Linear And Linear Models

2018 ◽  
Vol 61 (1) ◽  
pp. 29-38 ◽  
Author(s):  
Karla de Jesus ◽  
Helon V. H. Ayala ◽  
Kelly de Jesus ◽  
Leandro dos S. Coelho ◽  
Alexandre I.A. Medeiros ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Linear Model ◽  
Mean Absolute Percentage Error ◽  
Validation Dataset ◽  
Percentage Error ◽  
Start Time ◽  
Absolute Percentage Error ◽  
Non Linear ◽  
Artificial Neural

AbstractOur aim was to compare non-linear and linear mathematical model responses for backstroke start performance prediction. Ten swimmers randomly completed eight 15 m backstroke starts with feet over the wedge, four with hands on the highest horizontal and four on the vertical handgrip. Swimmers were videotaped using a dual media camera set-up, with the starts being performed over an instrumented block with four force plates. Artificial neural networks were applied to predict 5 m start time using kinematic and kinetic variables and to determine the accuracy of the mean absolute percentage error. Artificial neural networks predicted start time more robustly than the linear model with respect to changing training to the validation dataset for the vertical handgrip (3.95 ± 1.67 vs. 5.92 ± 3.27%). Artificial neural networks obtained a smaller mean absolute percentage error than the linear model in the horizontal (0.43 ± 0.19 vs. 0.98 ± 0.19%) and vertical handgrip (0.45 ± 0.19 vs. 1.38 ± 0.30%) using all input data. The best artificial neural network validation revealed a smaller mean absolute error than the linear model for the horizontal (0.007 vs. 0.04 s) and vertical handgrip (0.01 vs. 0.03 s). Artificial neural networks should be used for backstroke 5 m start time prediction due to the quite small differences among the elite level performances.

scholarly journals Wavelets-based non-linear model for real-time daily flow forecasting in Krishna River

2013 ◽  
Vol 15 (3) ◽  
pp. 1022-1041 ◽  
Author(s):  
R. Maheswaran ◽  
Rakesh Khosa
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Linear Model ◽  
Volterra Model ◽  
Lead Times ◽  
Discrete Wavelet ◽  
Promising Alternative ◽  
Daily Streamflow ◽  
Non Linear ◽  
Artificial Neural

In this study, a multi-scale non-linear model based on coupling a discrete wavelet transform (DWT) and the second-order Volterra model, i.e. the wavelet Volterra coupled (WVC) model, is applied for daily inflow forecasting at Krishna Agraharam, Krishna River, India. The relative performance of the WVC model was compared with regular artificial neural networks (ANN), wavelet-artificial neural networks (WA-ANN) models and other baseline models such as auto-regressive moving average with exogenous variables (ARMAX) for lead times of 1–5 days. The models were applied for the forecasting of daily streamflow at Krishna Agraharam Station at Krishna River. The WVC performed very well, especially when compared with the WA-ANN model for lead times of 4 and 5 days. The results indicate that the WVC model is a promising alternative to the other traditional models for short-term flow forecasting.

scholarly journals Over-parameterisation, a major obstacle to the use of artificial neural networks in hydrology?

2003 ◽  
Vol 7 (5) ◽  
pp. 693-706 ◽  
Author(s):  
E. Gaume ◽  
R. Gosset
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Conceptual Model ◽  
Linear Model ◽  
Case Studies ◽  
Stream Flow ◽  
River Flow ◽  
Prediction Models ◽  
Rainfall Runoff ◽  
Artificial Neural

Abstract. Recently Feed-Forward Artificial Neural Networks (FNN) have been gaining popularity for stream flow forecasting. However, despite the promising results presented in recent papers, their use is questionable. In theory, their “universal approximator‿ property guarantees that, if a sufficient number of neurons is selected, good performance of the models for interpolation purposes can be achieved. But the choice of a more complex model does not ensure a better prediction. Models with many parameters have a high capacity to fit the noise and the particularities of the calibration dataset, at the cost of diminishing their generalisation capacity. In support of the principle of model parsimony, a model selection method based on the validation performance of the models, "traditionally" used in the context of conceptual rainfall-runoff modelling, was adapted to the choice of a FFN structure. This method was applied to two different case studies: river flow prediction based on knowledge of upstream flows, and rainfall-runoff modelling. The predictive powers of the neural networks selected are compared to the results obtained with a linear model and a conceptual model (GR4j). In both case studies, the method leads to the selection of neural network structures with a limited number of neurons in the hidden layer (two or three). Moreover, the validation results of the selected FNN and of the linear model are very close. The conceptual model, specifically dedicated to rainfall-runoff modelling, appears to outperform the other two approaches. These conclusions, drawn on specific case studies using a particular evaluation method, add to the debate on the usefulness of Artificial Neural Networks in hydrology. Keywords: forecasting; stream-flow; rainfall-runoff; Artificial Neural Networks

scholarly journals Analysis of Cantilever Beam Deflection under Uniformly Distributed Load using Artificial Neural Networks

2019 ◽  
Vol 255 ◽  
pp. 06004
Author(s):  
T.M.Y.S Tuan Ya ◽  
Reza Alebrahim ◽  
Nadziim Fitri ◽  
Mahdi Alebrahim
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cantilever Beam ◽  
Training Data ◽  
Beam Deflection ◽  
Problem Solver ◽  
Distributed Load ◽  
Uniformly Distributed Load ◽  
Non Linear ◽  
Artificial Neural

In this study the deflection of a cantilever beam was simulated under the action of uniformly distributed load. The large deflection of the cantilever beam causes the non-linear behavior of beam. The prupose of this study is to predict the deflection of a cantilever beam using Artificial Neural Networks (ANN). The simulation of the deflection was carried out in MATLAB by using 2-D Finite Element Method (FEM) to collect the training data for the ANN. The predicted data was then verified again through a non linear 2-D geometry problem solver, FEM. Loads in different magnitudes were applied and the non-linear behaviour of the beam was then recorded. It was observed that, there is a close agreement between the predicted data from ANN and the results simulated in the FEM.

scholarly journals Hybrid Spectral Unmixing: Using Artificial Neural Networks for Linear/Non-Linear Switching

2017 ◽  
Vol 9 (8) ◽  
pp. 775 ◽  
Author(s):  
Asmau Ahmed ◽  
Olga Duran ◽  
Yahya Zweiri ◽  
Mike Smith
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Spectral Unmixing ◽  
Non Linear ◽  
Artificial Neural

Elevating Model Predictive Control Using Feedforward Artificial Neural Networks: A Review

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Senthil Kumar Arumugasamy ◽  
Zainal Ahmad
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Model Based Control ◽  
Model Based ◽  
Non Linear ◽  
Artificial Neural ◽  
Neural Predictive Control

Process control in the field of chemical engineering has always been a challenging task for the chemical engineers. Hence, the majority of processes found in the chemical industries are non-linear and in these cases the performance of the linear models can be inadequate. Recently a promising alternative modelling technique, artificial neural networks (ANNs), has found numerous applications in representing non-linear functional relationships between variables. A feedforward multi-layered neural network is a highly connected set of elementary non-linear neurons. Model-based control techniques were developed to obtain tighter control. Many model-based control schemes have been proposed to incorporate a process model into a control system. Among them, model predictive control (MPC) is the most common scheme. MPC is a general and mathematically feasible scheme to integrate our knowledge about a target, process controller design and operation, which allows flexible and efficient exploitation of our understanding of a target, and thus produces optimal performance of a system under various constraints. The need to handle some difficult control problems has led us to use ANN in MPC and has recently attracted a great deal of attention. The efficacy of the neural predictive control with the ability to perform comparably to the non linear neural network strategy in both set point tracking and disturbance rejection proves to have less computation expense for the neural predictive control. The neural network model predictive control (NNMPC) method has less perturbations and oscillations when dealing with noise as compared to the PI controllers.

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