Dynamic ensemble deep echo state network for significant wave height forecasting
Forecasts of the wave heights can assist in the data-driven control of wave energy systems. However, the dynamic properties and extreme fluctuations of the historical observations pose challenges to the construction of forecasting models. This paper proposes a novel dynamic ensemble deep Echo state...
Saved in:
Main Authors: | Gao, Ruobin, Li, Ruilin, Hu, Minghui, Suganthan, Ponnuthurai Nagaratnam, Yuen, Kum Fai |
---|---|
Other Authors: | School of Civil and Environmental Engineering |
Format: | Article |
Language: | English |
Published: |
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/170385 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Significant wave height forecasting using hybrid ensemble deep randomized networks with neurons pruning
by: Gao, Ruobin, et al.
Published: (2023) -
Online dynamic ensemble deep random vector functional link neural network for forecasting
by: Gao, Ruobin, et al.
Published: (2024) -
Random vector functional link neural network based ensemble deep learning for short-term load forecasting
by: Gao, Ruobin, et al.
Published: (2023) -
Bayesian optimization based dynamic ensemble for time series forecasting
by: Du, Liang, et al.
Published: (2022) -
Ship order book forecasting by an ensemble deep parsimonious random vector functional link network
by: Cheng, Ruke, et al.
Published: (2024)