Regularized Extreme Learning Machine Ensemble Using Bagging for Tropical Cyclone Tracks Prediction

被引：1

作者：

Zhang, Jun ^{[1
]}

Jin, Jian ^{[1
]}

机构：

[1] East China Normal Univ, Dept Comp Sci & Technol, 3363 North Zhongshan Rd, Shanghai 200062, Peoples R China

来源：

INTELLIGENCE SCIENCE AND BIG DATA ENGINEERING | 2018年 / 11266卷

关键词：

Regularized extreme learning machine; Bagging; Quadratic programming; Tropical Cyclone Tracks; REGRESSION;

D O I：

10.1007/978-3-030-02698-1_18

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper aims to improve the prediction accuracy of Tropical Cyclone Tracks (TCTs) over the South China Sea (SCS) and its coastal regions with 24 h lead time. The model proposed in this paper is a regularized extreme learning machine (ELM) ensemble using bagging. A new method is proposed in this paper to solve lasso and elastic net problem in ELM, which turns the original problem into familiar quadratic programming (QP) problem. The forecast error of TCTs data set is the distance between real position and forecast position. Compared with the stepwise regression method widely used in TCTs, 16.49km accuracy improvement is obtained by our model. Results show that the regularized ELM ensemble using bagging has a better generalization capactity on TCTs data set.

引用

页码：203 / 215

页数：13

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