Modeling of flood water level prediction using improved RBFNN structure

Machine learning models are implemented to perform tasks that human beings have difficulty completing. The analysis and prediction of players' performance of specific athletic tasks have increasing importance in both game and training planning. The diversity and complexity of specific types of athletic performance and the mostly nonlinear relationships between them make analysis and prediction tasks complicated when using conventional methods. Therefore, the use of effective machine learning models may contribute to the ability to achieve high accuracy predictions of players' athletic performance. The aim of this study was to evaluate different machine learning models for predicting particular types of athletic performance in female handball players and to determine the significant factors influencing predicted performances by using the superior model. Linear regression, decision tree, support vector regression, radialbasis function neural network, backpropagation neural network and long short-term memory neural network models were implemented to predict the performance of female handball players in countermovement jumps with hands-free and hands-on-hips, 10 meter and 20-meter sprints, a 20-meter shuttle run test and a handball agility specific test. A total of 23 properties and measurements of attributes and 118 instances of training patterns were recorded for each machine learning models. The results showed that the radial-basis function neural network outperformed the other models and was capable of predicting the studied types of athletic performance with R 2 scores between 0.86 and 0.97. Finally, significant factors influencing predicted performance were determined by retraining the superior model. This is one of the first studies using machine learning in sport sciences for handball players, and the results are encouraging for future studies. INDEX TERMS Artificial intelligence, athletic performance, machine learning models, radial-basis function neural network.

show abstract

“…RBFNNs have been implemented in several comparative studies aimed at performing classification and/or prediction problems [33]- [35].…”

Section: ) Radial-basis Function Neural Networkmentioning

confidence: 99%

Performance Prediction and Evaluation in Female Handball Players Using Machine Learning Models

Oytun¹,

Tınazcı²,

Şekeroğlu³

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…RBFNN is a kind of neural network that can be used both for classification and prediction problems [22][23]. It strictly has a single hidden layer and uses Radial Basis Functions in hidden neurons on this layer.…”

Section: Radial Basis Function Neural Networkmentioning

confidence: 99%

La Inteligencia Artificial en Educación: aplicación en la evaluación del desempeño del alumno.

Şekeroğlu¹,

Dimililer²,

Tuncal.³

2019

dilemas

View full text Add to dashboard Cite

La educación ha ganado importancia para acelerar el desarrollo de los países mediante la educación personalizada, etc., e impulsar su adquisición de conocimiento. Si bien es tarea difícil analizar a cada estudiante de forma independiente, los enfoques de Inteligencia Artificial y aprendizaje automático proporcionan soluciones rápidas con alta precisión para ayudar a los humanos. Este artículo presenta experimentos con diferentes conjuntos de datos educativos para predecir y clasificar el desempeño de los estudiantes, considerándose varios modelos de aprendizaje automático para cada problema y los resultados muestran que la Inteligencia Artificial puede ayudar a los instructores a mejorar la educación personalizada antes o durante el semestre activo. Se analiza que la red neuronal de la función de base radial supera a otros modelos considerados.

show abstract

“…The radial basis function neural network (RBFNN) is considered to be one of the answers for the described task. Those networks are widely implemented in many fields of science [41][42][43][44][45][46]. Because of their simplicity, they are often used in non-linear systems, solving mathematical equations, or estimating state variables of particular systems.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Sliding Mode Control Based on a Radial Neural Model Applied for an Electric Drive with an Elastic Shaft

Kaczmarczyk,

Stanislawski,

Szrek

et al. 2024

Energies

View full text Add to dashboard Cite

External disturbances, uncertainties, and nonlinear behavior are problems that are commonly encountered by control system designers. In order to save on energy and materials, mechanical structures have become lighter and more flexible, which only exacerbates the control problem. To resolve this issue, robust and adaptive control strategies have been proposed and have recently gained a lot of interest in modern scientific literature. This article proposes a combination of both approaches: a sliding mode—radial basis function neural network controller applied to an electrical drive with a sophisticated mechanical structure. The proposed sliding surface provides robustness against parameter uncertainties, while the neural network adjusts itself to the current state of the drive and mitigates the oscillations resulting from the elastic connection with the load machine. This article proves the stability of the proposed control algorithm in the sense of Lyapunov, provides an in-depth numerical analysis, and compares those results with the experimental tests. The algorithm was implemented in a 1103 dSPACE fast-prototyping card and was used to control a 0.5 kW DC motor connected to the load machine by a long (thin) steel shaft.

show abstract

Modeling of flood water level prediction using improved RBFNN structure

Cited by 9 publications

References 13 publications

Performance Prediction and Evaluation in Female Handball Players Using Machine Learning Models

Performance Prediction and Evaluation in Female Handball Players Using Machine Learning Models

La Inteligencia Artificial en Educación: aplicación en la evaluación del desempeño del alumno.

Adaptive Sliding Mode Control Based on a Radial Neural Model Applied for an Electric Drive with an Elastic Shaft

Contact Info

Product

Resources

About