Particle Swarm Optimization-Least Squares Support Vector Regression with Multi-Scale Wavelet Kernel

被引：0

作者：

Wang, Qin ^{[1
]}

Shen, Yuantong ^{[2
]}

机构：

[1] Hainan Med Coll, Dept Informat Technol, Haikou, Peoples R China

[2] China Univ Geosci, Sch Math & Phys, Wuhan 430074, Peoples R China

来源：

2013 SIXTH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID), VOL 1 | 2013年

关键词：

least squares support vector regression; multi-scale; wavelet kernel; particle swarm optimization; strong noise;

D O I：

10.1109/ISCID.2013.49

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A novel regression model combining least squares support vector regression (LS-SVR) with multi-scale wavelet kernel and particle swarm optimization (PSO) was presented in this paper, and applied to the approximation of non-stationary dataset and those continuous functions polluted by strong noise. Support vector kernel function with the multi-resolution characteristics was employed, such that LS-SVR with multi-scale wavelet kernel can estimate each details of target function accurately. The experimental results show that the proposed method is effective and feasible.

引用

页码：169 / 172

页数：4

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