Classification of stress of automobile drivers using Radial Basis Function Kernel Support Vector Machine

2014 ◽  
Author(s):  
Karthik Soman ◽  
A Sathiya ◽  
N Suganthi
Keyword(s):  
Support Vector Machine ◽  
Radial Basis Function ◽  
Basis Function ◽  
Support Vector ◽  
Radial Basis Function Kernel ◽  
Kernel Support Vector Machine ◽  
Radial Basis

Adaptive data-driven nonlinear synchro squeezed transform with single class radial basis function kernel support vector machine applied to wind turbine planetary gearbox fault diagnostics

2019 ◽  
Vol 234 (7) ◽  
pp. 1015-1025
Author(s):  
R Uma Maheswari ◽  
R Umamaheswari
Keyword(s):  
Support Vector Machine ◽  
High Resolution ◽  
Radial Basis Function ◽  
Amplitude Modulation ◽  
Basis Function ◽  
Support Vector ◽  
Radial Basis Function Kernel ◽  
Single Class ◽  
Kernel Support Vector Machine ◽  
Radial Basis

Planetary stage gears operated at low rotational speed and varying wind speed result variation in load. Variable speed and variable load induce nonstationary operating conditions. Vibration signal measured from Wind power gear transmission systems are embedded with multiple sources of vibration and attenuated considerably as it travels from source of vibration to measuring point. Efficacious multi-component decomposition without mode mixing ensures the accurate fault signature recognition. Synchro squeezing transform is the promising tool that represents the ridges with high resolution in time as well as in frequency axis. An efficient vibration analysis technique, short windowed Fourier synchro squeezing transform with nonlinear radial basis function kernel support vector machine is proposed to detect the mechanical faults in low speed planetary stage of wind turbines. Raw vibration is modeled in time–frequency plane to extract fault pattern signatures effectively with high resolution by adapting an empirical nonlinear tool synchro squeezing transforms. Amplitude modulation and frequency modulation parameters are sculpted from instantaneous amplitude and instantaneous phase, frequency. Hybrid feature space with signal attributes, statistical moments, and randomness measures are extricated from amplitude modulation-frequency modulation components. Single class radial basis function support vector machine is trained with hybrid features. The fault detection accuracy of the proposed method is compared with the standard variants of empirical mode decomposition. The proposed short windowed Fourier synchro squeezing transform-radial basis function kernel support vector machine shows 98.2% accuracy, 98% sensitivity, and 98% specificity.

An Empirical Analysis of Evolved Radial Basis Function Networks and Support Vector Machines with Mixture of Kernels

2015 ◽  
Vol 24 (04) ◽  
pp. 1550013 ◽  
Author(s):  
Ch. Sanjeev Kumar Dash ◽  
Pulak Sahoo ◽  
Satchidananda Dehuri ◽  
Sung-Bae Cho
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machine ◽  
Radial Basis Function ◽  
Basis Function ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Imbalanced Dataset ◽  
Radial Basis

Classification is one of the most fundamental and formidable tasks in many domains including biomedical. In biomedical domain, the distributions of data in most of the datasets into predefined number of classes is significantly different (i.e., the classes are distributed unevenly). Many mathematical, statistical, and machine learning approaches have been developed for classification of biomedical datasets with a varying degree of success. This paper attempts to analyze the empirical performance of two forefront machine learning algorithms particularly designed for classification problem by adding some novelty to address the problem of imbalanced dataset. The evolved radial basis function network with novel kernel and support vector machine with mixture of kernels are suitably designed for the purpose of classification of imbalanced dataset. The experimental outcome shows that both algorithms are promising compared to simple radial basis function neural networks and support vector machine, respectively. However, on an average, support vector machine with mixture kernels is better than evolved radial basis function neural networks.

Sign in / Sign up

Export Citation Format

Share Document