Random vector functional link neural network based deep learning for regression

The deep RVFLs are inspired by the principles of the Random Vector Functional Link (RVFL) neural network. Like RVFL, the weights of the hidden layers of the deep RVFLs (dRVFL and edRVFL) are stochastically generated within a suitable range and kept constant throughout the training. In this paper, we...

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Main Author: Chion, Jet Herng
Other Authors: Ponnuthurai Nagaratnam Suganthan
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2020
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Online Access:https://hdl.handle.net/10356/140217
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spelling sg-ntu-dr.10356-1402172023-07-07T18:51:44Z Random vector functional link neural network based deep learning for regression Chion, Jet Herng Ponnuthurai Nagaratnam Suganthan School of Electrical and Electronic Engineering epnsugan@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Electrical and electronic engineering The deep RVFLs are inspired by the principles of the Random Vector Functional Link (RVFL) neural network. Like RVFL, the weights of the hidden layers of the deep RVFLs (dRVFL and edRVFL) are stochastically generated within a suitable range and kept constant throughout the training. In this paper, we test and evaluate the performances of the recently proposed deep RVFLs neural networks on regression problems. Through the comprehensive evaluation on 29 different UCI datasets, we show that the performances of both dRVFL and edRVFL are significantly better than the RVFL variant (OPE-RVFL) in [1] and on par with backpropagation based Deep Neural Network. Furthermore, we identify Sigmoid as the most suitable activation function for regression tasks. Finally, we propose three new deep RVFLs variants (Deep Boosted dRVFL, edRVFL+ and Two-stage edRVFL+R) which show significant improvement over the vanilla deep RVFLs. Two-stage edRVFL+R is identified as the best performing algorithm among the three newly proposed variants as it performs better than the deep neural network in 22 out of the 29 UCI datasets. The robustness of the Two-stage edRVFL+R is further confirmed by the fact that it outperforms another method (Parallel metaheuristic-ensemble of heterogeneous feedforward neural networks ) proposed in [2] by a large margin in 11 out of 14 selected datasets. Bachelor of Engineering (Electrical and Electronic Engineering) 2020-05-27T07:04:08Z 2020-05-27T07:04:08Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/140217 en A1131-191 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Engineering::Electrical and electronic engineering
spellingShingle Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Engineering::Electrical and electronic engineering
Chion, Jet Herng
Random vector functional link neural network based deep learning for regression
description The deep RVFLs are inspired by the principles of the Random Vector Functional Link (RVFL) neural network. Like RVFL, the weights of the hidden layers of the deep RVFLs (dRVFL and edRVFL) are stochastically generated within a suitable range and kept constant throughout the training. In this paper, we test and evaluate the performances of the recently proposed deep RVFLs neural networks on regression problems. Through the comprehensive evaluation on 29 different UCI datasets, we show that the performances of both dRVFL and edRVFL are significantly better than the RVFL variant (OPE-RVFL) in [1] and on par with backpropagation based Deep Neural Network. Furthermore, we identify Sigmoid as the most suitable activation function for regression tasks. Finally, we propose three new deep RVFLs variants (Deep Boosted dRVFL, edRVFL+ and Two-stage edRVFL+R) which show significant improvement over the vanilla deep RVFLs. Two-stage edRVFL+R is identified as the best performing algorithm among the three newly proposed variants as it performs better than the deep neural network in 22 out of the 29 UCI datasets. The robustness of the Two-stage edRVFL+R is further confirmed by the fact that it outperforms another method (Parallel metaheuristic-ensemble of heterogeneous feedforward neural networks ) proposed in [2] by a large margin in 11 out of 14 selected datasets.
author2 Ponnuthurai Nagaratnam Suganthan
author_facet Ponnuthurai Nagaratnam Suganthan
Chion, Jet Herng
format Final Year Project
author Chion, Jet Herng
author_sort Chion, Jet Herng
title Random vector functional link neural network based deep learning for regression
title_short Random vector functional link neural network based deep learning for regression
title_full Random vector functional link neural network based deep learning for regression
title_fullStr Random vector functional link neural network based deep learning for regression
title_full_unstemmed Random vector functional link neural network based deep learning for regression
title_sort random vector functional link neural network based deep learning for regression
publisher Nanyang Technological University
publishDate 2020
url https://hdl.handle.net/10356/140217
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