Rule Extraction from Support Vector Machine and its Application to Hot-Dip Galvanizing

2013 ◽  
Vol 572 ◽  
pp. 300-303
Author(s):  
Jian Guo Wang ◽  
Bin Yang ◽  
Wen Xing Zhang ◽  
Bo Qin
Keyword(s):  
Support Vector Machine ◽  
Production Control ◽  
Raw Materials ◽  
Zinc Coating ◽  
Quantitative Relationship ◽  
Rule Extraction ◽  
Quality Parameters ◽  
Support Vector ◽  
Control Parameters ◽  
Input Variables

A new rule extraction algorithm based on convex hull for strip hot-dip galvanizing process monitoring is proposed in this paper. It overcomes the black-box problem of support vector machine. The zinc coating weight is used as the investigated subject. The sample datasets are trained by support vector machine rule extraction method, and the quantitative relationship can be obtained in the form of knowledge rules among input variables (such as the parameters of raw materials and control parameters of production) and output ones (the quality parameters), with which the production control parameters can be set and updated easily.

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.

Surface Roughness Prediction Modelling for WEDM of AA6063 Using Support Vector Machine Technique

2019 ◽  
Vol 969 ◽  
pp. 607-612 ◽  
Author(s):  
Thakur Singh ◽  
Pawan Kumar ◽  
Joy Prakash Misra
Keyword(s):  
Surface Roughness ◽  
Support Vector Machine ◽  
Research Work ◽  
Smart Manufacturing ◽  
Support Vector ◽  
Response Parameter ◽  
Roughness Prediction ◽  
Input Variables ◽  
Pulse On Time ◽  
Pulse Off Time

This research work presents an incorporated approach to modelling of WEDM of AA6063 (armour applications) using support vector machine technique. The experimental investigation has been carried out with four input variables namely pulse-on-time (Pon), pulse-off-time (Poff), servo-voltage (VS) and peak-current (IP). Surface roughness is measured as response parameter. The experimental runs are designed according to 3k full factorial design (k is number of input variables). It is apparent from this study that values anticipated by developed model are found closer to experimental results. Thus, it ensures appropriateness of model for prediction purpose and smart manufacturing. Machined surfaces are also examined by SEM to critically evaluate the process.

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