Analogy of support vector machine and linear regression models in surface roughness prediction

Journal of Physics Conference Series ◽

10.1088/1742-6596/1710/1/012005 ◽

2020 ◽

Vol 1710 ◽

pp. 012005

Author(s):

Z. Hweju ◽

K. Abou-El-Hossein

Keyword(s):

Surface Roughness ◽

Support Vector Machine ◽

Linear Regression ◽

Regression Models ◽

Support Vector ◽

Linear Regression Models ◽

Surface Roughness Prediction ◽

Roughness Prediction

Download Full-text

Prediction of COVID-19 pandemic measuring criteria using support vector machine, prophet and linear regression models in Indian scenario

Journal of Interdisciplinary Mathematics ◽

10.1080/09720502.2020.1833458 ◽

2020 ◽

pp. 1-20

Author(s):

Amit Kumar Gupta ◽

Vijander Singh ◽

Priya Mathur ◽

Carlos M. Travieso-Gonzalez

Keyword(s):

Support Vector Machine ◽

Linear Regression ◽

Regression Models ◽

Support Vector ◽

Linear Regression Models ◽

Indian Scenario

Download Full-text

Model structure learning: A support vector machine approach for LPV linear-regression models

IEEE Conference on Decision and Control and European Control Conference ◽

10.1109/cdc.2011.6160564 ◽

2011 ◽

Author(s):

Roland Toth ◽

Vincent Laurain ◽

Wei Xing Zheng ◽

Kameshwar Poolla

Keyword(s):

Support Vector Machine ◽

Linear Regression ◽

Regression Models ◽

Structure Learning ◽

Support Vector ◽

Model Structure ◽

Linear Regression Models

Download Full-text

Surface roughness prediction as a classification problem using support vector machine

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-017-0165-9 ◽

2017 ◽

Vol 92 (1-4) ◽

pp. 803-815 ◽

Author(s):

Issam Abu-Mahfouz ◽

Omar El Ariss ◽

A. H. M. Esfakur Rahman ◽

Amit Banerjee

Keyword(s):

Surface Roughness ◽

Support Vector Machine ◽

Classification Problem ◽

Support Vector ◽

Surface Roughness Prediction ◽

Roughness Prediction

Download Full-text

A System Identification Method for Linear Regression Models Based on Support Vector Machine

Transactions of the Society of Instrument and Control Engineers ◽

10.9746/sicetr1965.37.1189 ◽

2001 ◽

Vol 37 (12) ◽

pp. 1189-1191 ◽

Author(s):

Shuichi ADACHI ◽

Tomonori OGAWA ◽

Ryugo KONNO

Keyword(s):

Support Vector Machine ◽

System Identification ◽

Linear Regression ◽

Regression Models ◽

Support Vector ◽

Linear Regression Models ◽

System Identification Method ◽

Identification Method

Download Full-text

Use of least square support vector machine in surface roughness prediction model

10.1117/12.716199 ◽

2006 ◽

Author(s):

Hua Dong ◽

Dehui Wu ◽

Haitao Su

Keyword(s):

Surface Roughness ◽

Support Vector Machine ◽

Prediction Model ◽

Least Square ◽

Support Vector ◽

Surface Roughness Prediction ◽

Roughness Prediction

Download Full-text

A non-contact approach for surface roughness prediction in CNC turning using a linear regression model

Materials Today Proceedings ◽

10.1016/j.matpr.2019.12.029 ◽

2020 ◽

Vol 26 ◽

pp. 350-355 ◽

Author(s):

Dhiren R. Patel ◽

M.B. Kiran

Keyword(s):

Surface Roughness ◽

Linear Regression ◽

Regression Model ◽

Linear Regression Model ◽

Surface Roughness Prediction ◽

Cnc Turning ◽

Roughness Prediction

Download Full-text

Improving the Statistical Arbitrage Strategy in Intraday Trading by Combining Extreme Learning Machine and Support Vector Regression with Linear Regression Models

2013 IEEE 25th International Conference on Tools with Artificial Intelligence ◽

10.1109/ictai.2013.36 ◽

2013 ◽

Author(s):

Jarley Palmeira Nobrega ◽

Adriano Lorena Inacio De Oliveira

Keyword(s):

Linear Regression ◽

Support Vector Regression ◽

Extreme Learning Machine ◽

Regression Models ◽

Support Vector ◽

Linear Regression Models ◽

Statistical Arbitrage ◽

Arbitrage Strategy ◽

Learning Machine

Download Full-text

Surface roughness prediction using kernel locality preserving projection and Bayesian linear regression

Mechanical Systems and Signal Processing ◽

10.1016/j.ymssp.2020.107474 ◽

2021 ◽

Vol 152 ◽

pp. 107474

Author(s):

Dongdong Kong ◽

Junjiang Zhu ◽

Chaoqun Duan ◽

Lixin Lu ◽

Dongxing Chen

Keyword(s):

Surface Roughness ◽

Linear Regression ◽

Locality Preserving Projection ◽

Surface Roughness Prediction ◽

Locality Preserving ◽

Roughness Prediction ◽

Bayesian Linear Regression

Download Full-text

Bayesian linear regression for surface roughness prediction

Mechanical Systems and Signal Processing ◽

10.1016/j.ymssp.2020.106770 ◽

2020 ◽

Vol 142 ◽

pp. 106770 ◽

Author(s):

Dongdong Kong ◽

Junjiang Zhu ◽

Chaoqun Duan ◽

Lixin Lu ◽

Dongxing Chen

Keyword(s):

Surface Roughness ◽

Linear Regression ◽

Surface Roughness Prediction ◽

Roughness Prediction ◽

Bayesian Linear Regression

Download Full-text

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

Materials Science Forum ◽

10.4028/www.scientific.net/msf.969.607 ◽

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 ◽

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.

Download Full-text