Estimating the shift size in the process mean with support vector regression and neural networks

Cheng, Chuen‐Sheng; Chen, Pei-Wen; Huang, Kuo-Ko

doi:10.1016/j.eswa.2011.02.121

Cited by 23 publications

(15 citation statements)

References 14 publications

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“…Furthermore, the use of Support Vector Regression algorithms (SVMr) as metamodels has been applied to a large variety of regression problems, in many of them mixed with evolutionary algorithms [16,17].…”

Section: Literature Reviewmentioning

confidence: 99%

Aerodynamic Shape Design by Evolutionary Optimization and Support Vector Machines

Andrés-Pérez

Carro-Calvo

Salcedo‐Sanz

et al. 2016

Springer Tracts in Mechanical Engineering

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This paper proposes a computational methodology for the aerodynamic shape design of aeronautical configurations, aiming a broad and efficient exploration of the design space. A novel adaptive sampling technique focused on the global optimization problem, the Intelligent Estimation Search with Sequential Learning (IES-SL), is presented. This approach is based on the use of Support Vector Machines (SVMs) as the surrogate model for estimating the objective function, in combination with an evolutionary algorithm (EA) to enable the discovery of global optima. The proposed methodology is applied to improve the aerodynamic performance of a two-dimensional airfoil and a three-dimensional wing and results on surrogate model validation and optimization-focused sampling criteria are discussed.

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Section: Literature Reviewmentioning

confidence: 99%

Aerodynamic Shape Design by Evolutionary Optimization and Support Vector Machines

Andrés-Pérez

Carro-Calvo

Salcedo‐Sanz

et al. 2016

Springer Tracts in Mechanical Engineering

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“…Such information is however crucial for us, since it allows to further investigate the causes of the shifts. Although several methods have been developed to automatically predict the size of a shift after an alert (see for instance Cheng et al (2011) and the references therein), they are not adapted to data which are (simultaneously) non-normally distributed, serially correlated and contaminated by strong noise.…”

Section: Related Workmentioning

confidence: 99%

Nonparametric monitoring of sunspot number observations: a case study

Mathieu¹,

Lefèvre²,

Sachs³

et al. 2021

Preprint

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“…SVR maps the input data into a higher-dimensional feature space via nonlinear kernel functions [54]. The objective is to choose a vector of regression coefficients with a small norm, while minimizing the sum of the distances between the data points and the regression hyperplane in the higher-dimensional space [55].…”

Section: Support Vector Machine and Support Vector Regressionmentioning

confidence: 99%

Groundwater Prediction Using Machine-Learning Tools

et al. 2020

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Predicting groundwater availability is important to water sustainability and drought mitigation. Machine-learning tools have the potential to improve groundwater prediction, thus enabling resource planners to: (1) anticipate water quality in unsampled areas or depth zones; (2) design targeted monitoring programs; (3) inform groundwater protection strategies; and (4) evaluate the sustainability of groundwater sources of drinking water. This paper proposes a machine-learning approach to groundwater prediction with the following characteristics: (i) the use of a regression-based approach to predict full groundwater images based on sequences of monthly groundwater maps; (ii) strategic automatic feature selection (both local and global features) using extreme gradient boosting; and (iii) the use of a multiplicity of machine-learning techniques (extreme gradient boosting, multivariate linear regression, random forests, multilayer perceptron and support vector regression). Of these techniques, support vector regression consistently performed best in terms of minimizing root mean square error and mean absolute error. Furthermore, including a global feature obtained from a Gaussian Mixture Model produced models with lower error than the best which could be obtained with local geographical features.

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Estimating the shift size in the process mean with support vector regression and neural networks

Cited by 23 publications

References 14 publications

Aerodynamic Shape Design by Evolutionary Optimization and Support Vector Machines

Aerodynamic Shape Design by Evolutionary Optimization and Support Vector Machines

Nonparametric monitoring of sunspot number observations: a case study

Groundwater Prediction Using Machine-Learning Tools

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