Parsimonious wavelet kernel extreme learning machine

被引：0

作者：

Qin, Wang ^{[1
]}

Yuantong, Shen ^{[2
]}

Yu, Kuang ^{[3
]}

Qiang, Wu ^{[4
]}

Lin, Sun ^{[5
]}

机构：

[1] Department of Information Technology, Hainan Medical University, Haikou,571101, China

[2] School of Mathematics and Physics, China University of Geosciences, Wuhan,430074, China

[3] Department of Medical Physics, University of Nevada Las Vegas, Las Vegas,NV,89154, United States

[4] Affiliated Hospital of Hainan Medical University, Haikou,571101, China

[5] Foreign Languages Department, Hainan Medical University, Haikou,571101, China

来源：

Journal of Engineering Science and Technology Review | 2015年 / 8卷 / 05期

关键词：

Computation theory - Learning systems - Knowledge acquisition - Iterative methods;

D O I：

10.25103/jestr.085.28

中图分类号：

学科分类号：

摘要：

In this study, a parsimonious scheme for wavelet kernel extreme learning machine (named PWKELM) was introduced by combining wavelet theory and a parsimonious algorithm into kernel extreme learning machine (KELM). In the wavelet analysis, bases that were localized in time and frequency to represent various signals effectively were used. Wavelet kernel extreme learning machine (WELM) maximized its capability to capture the essential features in frequency-rich signals. The proposed parsimonious algorithm also incorporated significant wavelet kernel functions via iteration in virtue of Householder matrix, thus producing a sparse solution that eased the computational burden and improved numerical stability. The experimental results achieved from the synthetic dataset and a gas furnace instance demonstrated that the proposed PWKELM is efficient and feasible in terms of improving generalization accuracy and real time performance. © 2015 Kavala Institute of Technology.

引用

页码：219 / 226