Fuzzy rough regression with application to wind speed prediction

被引：44

作者：

An, Shuang ^{[1
,2
]}

Shi, Hong ^{[2
]}

Hu, Qinghua ^{[2
]}

Li, Xiaoqi ^{[1
]}

Dang, Jianwu ^{[2
]}

机构：

[1] Northeastern Univ, Shenyang 110004, Peoples R China

[2] Tianjin Univ, Tianjin 300072, Peoples R China

来源：

INFORMATION SCIENCES | 2014年 / 282卷

基金：

中国国家自然科学基金; 国家教育部科学基金资助; 中国博士后科学基金;

关键词：

Fuzzy rough set; Regression analysis; Fuzzy partition; Fuzzy approximation; Wind speed prediction; LINEAR-REGRESSION; REDUCTION; MODEL; SETS;

D O I：

10.1016/j.ins.2014.03.090

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

080201 [机械制造及其自动化];

摘要：

Accurate wind speed prediction is a prerequisite of large-scale wind power generation. There are several uncertain factors which degrade the performance of the current wind speed prediction systems. Fuzzy rough sets are considered as a powerful tool to deal with uncertainty, and have been widely discussed and applied in classification learning. In this work we describe a regression algorithm based on fuzzy rough sets, consisting of fuzzy partition, fuzzy approximation and estimation of regression values. In this algorithm, the training set is divided into k fuzzy classes with fuzzy partition, and then the predicted values of test samples are determined in the finite intervals with fuzzy rough approximation, finally they are estimated with lower and upper limits of the intervals. Numerical experiments on UCI data sets and wind speed prediction show the effectiveness of the proposed algorithm. (C) 2014 Elsevier Inc. All rights reserved.

引用

页码：388 / 400

页数：13

共 46 条

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On fuzzy-rough sets approach to feature selection [J].