|Table of Contents|

Research and Application of Twin Proximal Least Squares Support Vector Regression Model Based on Gaussian Noise(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2022年04期
Page:
19-28
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Research and Application of Twin Proximal Least Squares Support Vector Regression Model Based on Gaussian Noise
Author(s):
Yuan Qiuyun1; Zhang Shiguang2; Liu Shiqin3; Guo Shuangle4
(1.School of Information and Electronic Engineering,Shangqiu Institute of Technology,Shangqiu 476000,China)
(2.School of Information Engineering,Shandong Management University,Jinan 250357,China)
(3.College of Mathematics and Computer science,Hengshui University,Hengshui 053000,China)
(4.School of Information Engineering,Binzhou University,Binzhou 256600,China)
Keywords:
twin proximal least squares support vector regression; Gaussian noise; wind speed prediction; equality constraint
PACS:
TP301
DOI:
10.3969/j.issn.1672-1292.2022.04.003
Abstract:
Twin proximal least squares support vector regression(TPLSSVR)is a new regression model designed on the basis of PLSSVR model and TSVR model's double hyperplane concept. In this paper,we use the above model framework to build the twin proximal least squares support vector regression model based on Gaussian noise. The least square method is introduced and the regularization terms b21 and b22 are added respectively. It transforms an inequality constraint problem into two simpler equality constraint problems,which not only improves the generalization ability,but also effectively improves the prediction accuracy. In order to solve the parameter selection problem of the model,the particle swarm optimization algorithm with fast convergence speed and good robustness is selected to optimize its parameters. The new model is applied to artificial data set and wind speed data set,the experimental results show that the model has better prediction effect.

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Last Update: 2022-12-15