Data-driven modeling of vortex-induced vibration of a long-span suspension bridge using decision tree learning and support vector regression

2018 ◽  
Vol 172 ◽  
pp. 196-211 ◽  
Author(s):  
Shanwu Li ◽  
Shujin Laima ◽  
Hui Li
Keyword(s):  
Decision Tree ◽  
Support Vector Regression ◽  
Suspension Bridge ◽  
Data Driven ◽  
Support Vector ◽  
Decision Tree Learning ◽  
Vortex Induced Vibration ◽  
Long Span ◽  
Data Driven Modeling

scholarly journals Direction of Arrival Based on the Multioutput Least Squares Support Vector Regression Model

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Kai Huang ◽  
Ming-Yi You ◽  
Yun-Xia Ye ◽  
Bin Jiang ◽  
An-Nan Lu
Keyword(s):  
Least Squares ◽  
Support Vector Regression ◽  
Direction Of Arrival ◽  
Direction Finding ◽  
Training Data ◽  
Data Driven ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Model Training ◽  
Finding Method

The interferometer is a widely used direction-finding system with high precision. When there are comprehensive disturbances in the direction-finding system, some scholars have proposed corresponding correction algorithms, but most of them require hypothesis based on the geometric position of the array. The method of using machine learning that has attracted much attention recently is data driven, which can be independent of these assumptions. We propose a direction-finding method for the interferometer by using multioutput least squares support vector regression (MLSSVR) model. The application of this method includes the following: the construction of MLSSVR model training data, training and construction of the MLSSVR model, and the estimation of direction of arrival. Finally, the method is verified through numerical simulation. When there are comprehensive deviations in the system, the direction-finding accuracy can be effectively improved.

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