Prediction of Chlorophyll-a content using hybrid model of least squares support vector regression and radial basis function neural networks

A determinação do risco de cair é de suma importância na assistência à saúde do idoso, pois a ocorrência de quedas nessa população trazem consequências em vários aspectos. Ferramentas de aprendizado de máquinas têm sido cada vez mais empregadas com este fim. Portanto, o objetivo deste estudo foi investigar a viabilidade da utilização de sinais eletromiográficos e dinamométricos na classificação do risco de quedas em idosos via modelo least squares support vector regression (LSSVR). Trinta e um voluntários idosos foram avaliados com a Escala de Equilíbrio de Berg (EEB), eletromiografia e dinamometria do membro inferior dominante. Para o modelo LSSVR foram utilizados kernels do tipo linear, polinomial e radial basis function (RBF), além de validações cruzadas pelos métodos leave one out e K-fold. Ambos os sinais apresentaram erros médios baixos na maioria das execuções realizadas. Dessa forma, verificou-se que é possível classificar o risco de quedas em idosos por meio de sinais eletromiográficos e dinamométricos aplicados ao modelo LSSVR.

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Performance Comparison of Generalized Regression Network, Radial Basis Function Network and Support Vector Regression for Wind Power Forecasting

International Review on Modelling and Simulations (IREMOS) ◽

10.15866/iremos.v12i1.15781 ◽

2019 ◽

Vol 12 (1) ◽

pp. 16

Author(s):

Jyothi Varanasi ◽

Madan Mohan Tripathi

Keyword(s):

Radial Basis Function ◽

Support Vector Regression ◽

Wind Power ◽

Basis Function ◽

Radial Basis Function Network ◽

Performance Comparison ◽

Support Vector ◽

Wind Power Forecasting ◽

Radial Basis ◽

Function Network

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Design of radial basis function-based support vector regression in predicting the discharge coefficient of a side weir in a trapezoidal channel

Applied Water Science ◽

10.1007/s13201-019-0961-5 ◽

2019 ◽

Vol 9 (4) ◽

Cited By ~ 15

Author(s):

Hamed Azimi ◽

Hossein Bonakdari ◽

Isa Ebtehaj

Keyword(s):

Radial Basis Function ◽

Support Vector Regression ◽

Basis Function ◽

Discharge Coefficient ◽

Support Vector ◽

Side Weir ◽

Trapezoidal Channel ◽

Radial Basis

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Design of Reinforced Fuzzy Radial Basis Function Neural Networks Classifier Driven with the Aid of Iterative Learning Techniques and Support Vector-based Clustering

IEEE Transactions on Fuzzy Systems ◽

10.1109/tfuzz.2020.3001740 ◽

2020 ◽

pp. 1-1

Author(s):

Cheng Yang ◽

Sung-Kwun Oh ◽

Witold Pedrycz ◽

Zunwei Fu ◽

Bo Yang

Keyword(s):

Neural Networks ◽

Radial Basis Function ◽

Basis Function ◽

Iterative Learning ◽

Support Vector ◽

Learning Techniques ◽

Radial Basis

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Collapse susceptibility assessment using a support vector machine compared with back-propagation and radial basis function neural networks

Geomatics Natural Hazards and Risk ◽

10.1080/19475705.2020.1734101 ◽

2020 ◽

Vol 11 (1) ◽

pp. 510-534

Author(s):

Yuanyao Li ◽

Yifan Sheng ◽

Bo Chai ◽

Wei Zhang ◽

Taili Zhang ◽

...

Keyword(s):

Neural Networks ◽

Support Vector Machine ◽

Radial Basis Function ◽

Basis Function ◽

Back Propagation ◽

Support Vector ◽

Susceptibility Assessment ◽

Radial Basis

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COVID-19 Spread Pattern Using Support Vector Regression

PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic ◽

10.33558/piksel.v8i1.2024 ◽

2020 ◽

Vol 8 (1) ◽

pp. 67-74 ◽

Cited By ~ 1

Author(s):

Herlawati Herlawati

Keyword(s):

Radial Basis Function ◽

Support Vector Regression ◽

Soft Computing ◽

Basis Function ◽

Rapid Development ◽

Support Vector ◽

Growth Simulation ◽

Real Condition ◽

Radial Basis ◽

Spread Pattern

Pandemics are rare and happen in about 100 years period. Current pandemic, COVID-19, occurs in the industrial 4.0 era where there is a rapid development computation. Yet, the scientists in every country face difficulty in predicting the growth simulation of this pandemic. The paper tries to use a soft computing algorithm to predict the pattern of the COVID-19 pandemic in Indonesia. Support Vector Regression was used in Google Interactive Notebook with some kernels for comparison, i.e. radial basis function, linear and polynomial. The testing results showed that radial basis function outperformed other kernels as a regressor with some parameters should follows the real condition, i.e. gamma, c, and epsilon.

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Analysis of comparisons for Forecasting Gold Price using Neural Network, Radial Basis Function Network and Support Vector Regression

The 4th Joint International Conference on Information and Communication Technology, Electronic and Electrical Engineering (JICTEE) ◽

10.1109/jictee.2014.6804078 ◽

2014 ◽

Cited By ~ 1

Author(s):

Khanoksin Suranart ◽

Supaporn Kiattisin ◽

Adisorn Leelasantitham

Keyword(s):

Neural Network ◽

Radial Basis Function ◽

Support Vector Regression ◽

Basis Function ◽

Radial Basis Function Network ◽

Gold Price ◽

Support Vector ◽

Radial Basis ◽

Function Network

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Potential of radial basis function based support vector regression for global solar radiation prediction

Renewable and Sustainable Energy Reviews ◽

10.1016/j.rser.2014.07.108 ◽

2014 ◽

Vol 39 ◽

pp. 1005-1011 ◽

Cited By ~ 80

Author(s):

Zeynab Ramedani ◽

Mahmoud Omid ◽

Alireza Keyhani ◽

Shahaboddin Shamshirband ◽

Benyamin Khoshnevisan

Keyword(s):

Solar Radiation ◽

Radial Basis Function ◽

Support Vector Regression ◽

Basis Function ◽

Global Solar Radiation ◽

Support Vector ◽

Radial Basis

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Robust radial basis function networks based on least trimmed squares-support vector regression

2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS) ◽

10.1109/ifsa-nafips.2013.6608365 ◽

2013 ◽

Cited By ~ 1

Author(s):

Shun-Feng Su ◽

Jin-Tsong Jeng ◽

Yue-Shiang Liu ◽

Chen-Chia Chuang ◽

Imre J. Rudas

Keyword(s):

Radial Basis Function ◽

Support Vector Regression ◽

Basis Function ◽

Support Vector ◽

Radial Basis Function Networks ◽

Least Trimmed Squares ◽

Radial Basis

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