Robust penalized extreme learning machine regression with applications in wind speed forecasting

Journal article


Yang, Yang, Zhou, Hu, Gao, Yuchao, Wu, Jinran, Wang, You-Gan and Fu, Liya. (2022). Robust penalized extreme learning machine regression with applications in wind speed forecasting. Neural Computing and Applications. 34(1), pp. 391-407. https://doi.org/10.1007/s00521-021-06370-3
AuthorsYang, Yang, Zhou, Hu, Gao, Yuchao, Wu, Jinran, Wang, You-Gan and Fu, Liya
Abstract

In extreme learning machine (ELM) framework, the hidden layer setting determines its generalization ability; and in presence of outliers in the training set, weights between hidden layer and output layer based on the least squares would be overly estimated. To address these two problems in ELM implementation, we extend robust penalized statistical framework in ELM and propose a general robust penalized ELM, which consists of two components (robust loss function and regularization item), for regression to improve the efficiency of ELM training with more elegant neural network structure resulting in more accurate predictions. We investigate six different loss functions (l1-norm loss, l2-norm loss, Huber loss, Bisquare loss, exponential squared loss and Lncosh loss) and two regularization strategies (lasso penalty and ridge penalty). Furthermore, we present two training procedures for our robust penalized ELM via iterative reweighted least squares method with hyper-parameter setting by cross-validation with lasso penalty and ridge penalty, respectively. Finally, the proposed robust penalized ELM is employed in an ultra-short-term wind speed forecasting study, and our framework is confirmed in this specific application producing more effective predictions according to the multi-step forecasting performance.

Keywordsrobust loss function; regularization method; neural networks; prediction; iterative procedure
Year2022
JournalNeural Computing and Applications
Journal citation34 (1), pp. 391-407
PublisherSpringer
ISSN0941-0643
Digital Object Identifier (DOI)https://doi.org/10.1007/s00521-021-06370-3
Scopus EID2-s2.0-85112094304
Research or scholarlyResearch
Page range391-407
FunderAustralian Research Council
National Natural Science Foundation of China
Natural Science Foundation of Jiangsu Province
Nanjing University of Posts and Telecommunications
Postgraduate Research & Practice Innovation Program of Jiangsu Province
Publisher's version
License
All rights reserved
File Access Level
Controlled
Output statusPublished
Publication dates
Online09 Aug 2021
Publication process dates
Accepted26 Jul 2021
Deposited25 Aug 2022
ARC Funded ResearchThis output has been funded, wholly or partially, under the Australian Research Council Act 2001
Grant IDCE140100049
61873130
61833011
61833008
BK20191377
BK20191376
NY220102
NY220194
2020XZZ11
KYCX20 0821
SJCX21 0292
11871390
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