Applying input variables selection technique on input weighted support vector machine modeling for BOF endpoint prediction

2010 ◽  
Vol 23 (6) ◽  
pp. 1012-1018 ◽  
Author(s):  
Xinzhe Wang ◽  
Min Han ◽  
Jun Wang
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Selection Technique ◽  
Machine Modeling ◽  
Variables Selection ◽  
Endpoint Prediction ◽  
Input Variables

scholarly journals Three-Dimensional Site Characterization Model of Bangalore Using Support Vector Machine

2012 ◽  
Vol 2012 ◽  
pp. 1-10
Author(s):  
Pijush Samui
Keyword(s):  
Support Vector Machine ◽  
Three Dimensional ◽  
Site Characterization ◽  
Standard Penetration Test ◽  
Support Vector ◽  
Characterization Model ◽  
Svm Model ◽  
Input Variables ◽  
Learning Machine ◽  
A Site

The main objective of site characterization is the prediction of in situ soil properties at any half-space point at a site based on limited tests. In this study, the Support Vector Machine (SVM) has been used to develop a three dimensional site characterization model for Bangalore, India based on large amount of Standard Penetration Test. SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing ε-insensitive loss function. The database consists of 766 boreholes, with more than 2700 field SPT values () spread over 220 sq km area of Bangalore. The model is applied for corrected () values. The three input variables (, , and , where , , and are the coordinates of the Bangalore) were used for the SVM model. The output of SVM was the data. The results presented in this paper clearly highlight that the SVM is a robust tool for site characterization. In this study, a sensitivity analysis of SVM parameters (σ, , and ε) has been also presented.

Utilization of Classification Techniques for the Determination of Liquefaction Susceptibility of Soils

2016 ◽  
pp. 124-160 ◽  
Author(s):  
J. Jagan ◽  
Prabhakar Gundlapalli ◽  
Pijush Samui
Keyword(s):  
Support Vector Machine ◽  
Earthquake Engineering ◽  
Relevance Vector Machine ◽  
Least Square ◽  
Support Vector ◽  
Ground Acceleration ◽  
Geotechnical Earthquake Engineering ◽  
Liquefaction Susceptibility ◽  
Input Variables

The determination of liquefaction susceptibility of soil is a paramount project in geotechnical earthquake engineering. This chapter adopts Support Vector Machine (SVM), Relevance Vector Machine (RVM) and Least Square Support Vector Machine (LSSVM) for determination of liquefaction susceptibility based on Cone Penetration Test (CPT) from Chi-Chi earthquake. Input variables of SVM, RVM and LSSVM are Cone Resistance (qc) and Peak Ground Acceleration (amax/g). SVM, RVM and LSSVM have been used as classification tools. The developed SVM, RVM and LSSVM give equations for determination of liquefaction susceptibility of soil. The comparison between the developed models has been carried out. The results show that SVM, RVM and LSSVM are the robust models for determination of liquefaction susceptibility of soil.

OPTIMIZATION OF LEAST SQUARES SUPPORT VECTOR MACHINE TECHNIQUE USING GENETIC ALGORITHM FOR ELECTROENCEPHALOGRAM MULTI-DIMENSIONAL SIGNALS

2016 ◽  
Vol 78 (5-10) ◽  
Author(s):  
Farzana Kabir Ahmad ◽  
Abdullah Yousef Awwad Al-Qammaz ◽  
Yuhanis Yusof
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Computational Model ◽  
Least Squares ◽  
Support Vector ◽  
Feature Selection Technique ◽  
Selection Technique ◽  
Human Emotion ◽  
Valence And Arousal

Human-computer intelligent interaction (HCII) is a rising field of science that aims to refine and enhance the interaction between computer and human. Since emotion plays a vital role in human daily life, the ability of computer to interpret and response to human emotion is a crucial element for future intelligent system. Accordingly, several studies have been conducted to recognise human emotion using different technique such as facial expression, speech, galvanic skin response (GSR), or heart rate (HR). However, such techniques have problems mainly in terms of credibility and reliability as people can fake their feeling and response. Electroencephalogram (EEG) on the other has shown to be a very effective way in recognising human emotion as this technique records the brain activity of human and they can hardly be deceived by voluntary control. Regardless the popularity of EEG in recognizing human emotion, this study field is relatively challenging as EEG signal is nonlinear, involves myriad factors and chaotic in nature. These issues have led to high dimensional problem and poor classification results. To address such problems, this study has proposed a novel computational model, which consist of three main stages, namely a) feature extraction; b) feature selection and c) classifier. Discrete wavelet packet transform (DWPT) has been used to extract EEG signals feature and ultimately 204,800 features from 32 subject-independent have been obtained. Meanwhile, Genetic Algorithm (GA) and Least squares support vector machine (LS-SVM) have been used as a feature selection technique and classifier respectively. This computational model is tested on the common DEAP pre-processed EEG dataset in order to classify three levels of valence and arousal. The empirical results have shown that the proposed GA-LSSVM, has improved the classification results to 49.22% and 54.83% for valence and arousal respectively, whereas is it observed that 46.33% of valence and 48.30% of arousal classification were achieved when no feature selection technique is applied on the identical classifier

Application of Support Vector Machine Modeling on Phase Distribution in the Riser of an LSCFB Reactor

2014 ◽  
Vol 12 (1) ◽  
pp. 123-134 ◽  
Author(s):  
Shaikh A. Razzak ◽  
Muhammad I. Hossain ◽  
Syed M. Rahman ◽  
Mohammad M. Hossain
Keyword(s):  
Support Vector Machine ◽  
Circulating Fluidized Bed ◽  
Phase Distribution ◽  
Solid Phase ◽  
Correlation Coefficients ◽  
Absolute Error ◽  
Pilot Scale ◽  
Support Vector ◽  
Cross Sectional ◽  
Machine Modeling

Abstract Support vector machine (SVM) modeling approach is applied to predict the solids holdups distribution of a liquid–solid circulating fluidized bed (LSCFB) riser. The SVM model is developed/trained using experimental data collected from a pilot-scale LSCFB reactor. Two different size glass bead particles (500 μm (GB-500) and 1,290 μm (GB-1290)) are used as solid phase, and water is used as liquid phase. The trained model successfully predicted the experimental solids holdups of the LSCFB riser under different operating parameters. It is observed that the model predicted cross-sectional average of solids holdups in the axial directions and radial flow structure are well agreement with the experimental values. The goodness of the model prediction is verified by using different statistical performance indicators. For the both sizes of particles, the mean absolute error is found to be less than 5%. The correlation coefficients (0.998 for GB-500 and 0.994 for GB-1290) also show favorable indications of the suitability of SVM approach in predicting the solids holdup of the LSCFB system.

Sign in / Sign up

Export Citation Format

Share Document