Modeling M(3000)F2 based on extreme learning machine-Reference-Cited by-同舟云学术

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Modeling M(3000)F2 based on extreme learning machine

Published:2020-01 Issue:1 Volume:65 Page:107-114
ISSN:0273-1177
Container-title:Advances in Space Research
language:en
Short-container-title:Advances in Space Research

Author:

Bai Hongmei,Feng Feng^ORCID,Wang Jian^ORCID,Wu Taosuo

Funder

National Key R&D Plan

National Natural Science Foundation of China

Tianjin Research Program of Application Foundation and Advanced Technology

National 973 Program of China

Qingdao National Laboratory for Marine Science and Technology

Publisher

Elsevier BV

Subject

Space and Planetary Science,Aerospace Engineering,General Earth and Planetary Sciences,Atmospheric Science,Geophysics,Astronomy and Astrophysics

Reference31 articles.

1. A new intelligent method based on combination of VMD and ELM for short term wind power forecasting;Abdoos;Neurocomputing,2016

2. A neural network-based foF2 model for a single station in the polar cap;Athieno;Radio Sci.,2017

3. A prediction model of ionospheric foF2 based on extreme learning machine;Bai;Radio Sci.,2018

4. The International Reference Ionosphere 2012—A model of international collaboration;Bilitza;J. Space Weather Space Clim.,2014

5. International Reference Ionosphere 2016: From ionospheric climate to real-time weather predictions;Bilitza;Space Weather,2017

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1. A Near-Real-Time Forecasting Model of High-Frequency Radiowave Propagation Factor Fusion Based on the ICEEMDAN Decomposition and Bi-LSTM Methods;IEEE Transactions on Antennas and Propagation;2024-07

2. A Short-Term Forecast Method of Maximum Usable Frequency for HF Communication;IEEE Transactions on Antennas and Propagation;2023-06

3. An Explainable Dynamic Prediction Method for Ionospheric foF2 Based on Machine Learning;Remote Sensing;2023-02-24

4. A combination of ridge and Liu regressions for extreme learning machine;Soft Computing;2022-12-22

5. Approach for Extreme Learning Machine-Based Microwave Power Device Modeling;IEEE Access;2022

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