scholarly journals Financial Volatility Forecasting by Nonlinear Support Vector Machine Heterogeneous Autoregressive Model: Evidence from Nikkei 225 Stock Index

2011 ◽  
Vol 3 (4) ◽  
Author(s):  
Md. Ashraful Islam Khan
Keyword(s):  
Support Vector Machine ◽  
Autoregressive Model ◽  
Stock Index ◽  
Support Vector ◽  
Volatility Forecasting ◽  
Financial Volatility ◽  
Model Evidence ◽  
Nonlinear Support

scholarly journals Automatic Detection of Voltage Notches using Support Vector Machine

2021 ◽  
Vol 19 ◽  
pp. 528-533
Author(s):  
Rongzhen Qi ◽  
◽  
Olga Zyabkina ◽  
Daniel Agudelo Martinez ◽  
Jan Meyer
Keyword(s):  
Support Vector Machine ◽  
Domain Knowledge ◽  
Detection Efficiency ◽  
Support Vector ◽  
Svm Classifier ◽  
Automatic Method ◽  
Power Quality Disturbances ◽  
Characteristic Features ◽  
Comprehensive Framework ◽  
Nonlinear Support

This paper presents a comprehensive framework for voltage notch analysis and an automatic method for notch detection using a nonlinear support vector machine (SVM) classifier. A comprehensive simulation of the notch disturbance has been conducted to generate a diverse database. Based on domain knowledge and properties of power quality disturbances (PQDs), a set of characteristic features is extracted. After feature extraction, a set of most descriptive features has been selected with decision tree (DT) algorithm, and a nonlinear SVM classifier has been trained. Finally, the detection efficiency of the trained model is presented and discussed.

scholarly journals Structural Damage Detection Using Supervised Nonlinear Support Vector Machine

2021 ◽  
Vol 5 (11) ◽  
pp. 303
Author(s):  
Kian K. Sepahvand
Keyword(s):  
Support Vector Machine ◽  
Damage Detection ◽  
Structural Damage ◽  
Natural Frequencies ◽  
Training Data ◽  
Support Vector ◽  
Lightweight Structures ◽  
Straightforward Method ◽  
Classification Boundary ◽  
Nonlinear Support

Damage detection, using vibrational properties, such as eigenfrequencies, is an efficient and straightforward method for detecting damage in structures, components, and machines. The method, however, is very inefficient when the values of the natural frequencies of damaged and undamaged specimens exhibit slight differences. This is particularly the case with lightweight structures, such as fiber-reinforced composites. The nonlinear support vector machine (SVM) provides enhanced results under such conditions by transforming the original features into a new space or applying a kernel trick. In this work, the natural frequencies of damaged and undamaged components are used for classification, employing the nonlinear SVM. The proposed methodology assumes that the frequencies are identified sequentially from an experimental modal analysis; for the study propose, however, the training data are generated from the FEM simulations for damaged and undamaged samples. It is shown that nonlinear SVM using kernel function yields in a clear classification boundary between damaged and undamaged specimens, even for minor variations in natural frequencies.

Damaged Apple Sorting With mmWave Imaging and Nonlinear Support Vector Machine

2020 ◽  
Vol 68 (12) ◽  
pp. 8062-8071
Author(s):  
Flora Zidane ◽  
Jerome Lanteri ◽  
Laurent Brochier ◽  
Nadine Joachimowicz ◽  
Helene Roussel ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Nonlinear Support
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