Wind Speed Forecasting Using Improved Random Vector Functional Link Network

被引：0

作者：

Nhabangue, Moreira F. C. ^{[1
]}

Pillai, G. N. ^{[1
]}

机构：

[1] Indian Inst Technol Roorkee, Dept Elect Engn, Roorkee, Uttar Pradesh, India

来源：

2018 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI) | 2018年

关键词：

Random Vector Functional Link; Chebyshev Polynomials; Empirical Mode Decomposition; Wind speed prediction; EMPIRICAL MODE DECOMPOSITION; EXTREME LEARNING-MACHINE; REGRESSION; PREDICTION;

D O I：

暂无

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 proposes an improved Random Vector Functional Link Network (RVFL) for better performance in regression problems. The model applies the Chebyshev expansion to transform the direct links of the RVFL providing better mapping of nonlinear functions when compared with RVFL model. Two wind speed datasets are used for performance comparison with other models. The application of Chebyshev expansion in the RVFL enables the RVFL to have a lower number of activation nodes reducing its size with better performance. The models are also tested with their ensembled version by applying the empirical mode decomposition (EMD).

引用

页码：1744 / 1750

页数：7

共 17 条

[1] Robust regularized extreme learning machine for regression using iteratively reweighted least squares [J].

Chen, Kai ;

Lv, Qi ;

Lu, Yao ;

Dou, Yong .

NEUROCOMPUTING, 2017, 230 :345-358

[2] Regularized Extreme Learning Machine [J].

Deng, Wanyu ;

Zheng, Qinghua ;

Chen, Lin .

2009 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DATA MINING, 2009, :389-395

[3] Reinforced Extreme Learning Machines for Fast Robust Regression in the Presence of Outliers [J].

Frenay, Benoit ;

Verleysen, Michel .

IEEE TRANSACTIONS ON CYBERNETICS, 2016, 46 (12) :3351-3363

[4] Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions [J].

Huang, GB ;

Babri, HA .

IEEE TRANSACTIONS ON NEURAL NETWORKS, 1998, 9 (01) :224-229

[5] Extreme learning machine: Theory and applications [J].

Huang, Guang-Bin ;

Zhu, Qin-Yu ;

Siew, Chee-Kheong .

NEUROCOMPUTING, 2006, 70 (1-3) :489-501

[6] The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis [J].

Huang, NE ;

Shen, Z ;

Long, SR ;

Wu, MLC ;

Shih, HH ;

Zheng, QN ;

Yen, NC ;

Tung, CC ;

Liu, HH .

PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 1998, 454 (1971) :903-995

[7]

Li W. B. Joseph, 2010, USE CHEBYSHEV POLYNO, P29

[8] A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks [J].

Liu, Hui ;

Chen, Chao ;

Tian, Hong-qi ;

Li, Yan-fei .

RENEWABLE ENERGY, 2012, 48 :545-556

[9] Using artificial neural networks for temporal and spatial wind speed forecasting in Iran [J].

Noorollahi, Younes ;

Jokar, Mohammad Ali ;

Kalhor, Ahmad .

ENERGY CONVERSION AND MANAGEMENT, 2016, 115 :17-25

[10] A Novel Wind Power Forecast Model: Statistical Hybrid Wind Power Forecast Technique (SHWIP) [J].

Ozkan, Mehmet Baris ;

Karagoz, Pinar .

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2015, 11 (02) :375-387

← 1 2 →