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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/17053
Title: Wind Speed Forecasting Using Improved Random Vector Functional Link Network
Authors: Nhabangue M.F.C.
Pillai, Gopinatha Nath
Sundaram S.
Published in: Proceedings of 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018
Abstract: 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). © 2018 IEEE.
Citation: Proceedings of 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018, (2019), 1744- 1750
URI: https://doi.org/10.1109/SSCI.2018.8628822
http://repository.iitr.ac.in/handle/123456789/17053
Issue Date: 2019
Publisher: Institute of Electrical and Electronics Engineers Inc.
Keywords: Chebyshev Polynomials
Empirical Mode Decomposition
Random Vector Functional Link
Wind speed prediction
ISBN: 9.78154E+12
Author Scopus IDs: 57202991516
7005839948
Author Affiliations: Nhabangue, M.F.C., Department of Electrical Engineering, Indian Institute of Technology, Roorkee, Roorkee, India
Pillai, G.N., Department of Electrical Engineering, Indian Institute of Technology, Roorkee, Roorkee, India
Appears in Collections:Conference Publications [EE]

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