Simplified-Boost Reinforced Model-Based Complex Wind Signal Forecasting
Joint Authors
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-09-30
Country of Publication
Egypt
No. of Pages
16
Main Subjects
Abstract EN
Wind signal forecasting has become more and more crucial in the structural health monitoring system and wind engineering recently.
It is a challenging subject owing to the complicated volatility of wind signals.
The robustness and generalization of a predictor are significant as well as of high precision.
In this paper, an adaptive residual convolutional neural network (CNN) is developed, aiming at achieving not only high precision but also high adaptivity for various wind signals with varying complexity.
Afterwards, reinforced forecasting is adopted to enhance the robustness of the preliminary forecasting.
The preliminary forecast results by adaptive residual CNN are integrated with historical observed signals as the new input to reconstruct a new forecasting mapping.
Meanwhile, simplified-boost strategy is applied for more generalized results.
The results of multistep forecasting for five kinds of nonstationary non-Gaussian wind signals prove the more excellent adaptivity and robustness of the developed two-stage model compared with single models.
American Psychological Association (APA)
Lin, Qiushuang& Li, Chunxiang. 2020. Simplified-Boost Reinforced Model-Based Complex Wind Signal Forecasting. Advances in Civil Engineering،Vol. 2020, no. 2020, pp.1-16.
https://search.emarefa.net/detail/BIM-1125893
Modern Language Association (MLA)
Lin, Qiushuang& Li, Chunxiang. Simplified-Boost Reinforced Model-Based Complex Wind Signal Forecasting. Advances in Civil Engineering No. 2020 (2020), pp.1-16.
https://search.emarefa.net/detail/BIM-1125893
American Medical Association (AMA)
Lin, Qiushuang& Li, Chunxiang. Simplified-Boost Reinforced Model-Based Complex Wind Signal Forecasting. Advances in Civil Engineering. 2020. Vol. 2020, no. 2020, pp.1-16.
https://search.emarefa.net/detail/BIM-1125893
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1125893