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A feasibility study on applying meta-heuristic optimization and Gaussian process regression for predicting the performance of pantograph-catenary system
期刊论文
ACTA MECHANICA SINICA, 2024, 卷号: 40, 期号: 1, 页码: 12
作者:
Zhang MH(张莫晗)
;
Yin B(银波)
;
Sun ZX(孙振旭)
;
Bai, Ye
;
Yang GW(杨国伟)
收藏
  |  
浏览/下载:2/0
  |  
提交时间:2024/02/26
Pantograph-catenary system
Gaussian process regression
Surrogate model
Physical-based model
On the Investigation of Effective Factors on Electronic Structure Properties of Transition Metal Complexes: Robust Modeling Using GPR Approach
期刊论文
International Journal of Chemical Engineering, 2022, 卷号: 2022
作者:
Wang, Jianjun
;
Molla Jafari, Mohammad Mahdi
收藏
  |  
浏览/下载:21/0
  |  
提交时间:2022/04/21
Chemical bonds
Computation theory
Density functional theory
Electronic structure
Inorganic compounds
Spin dynamics
Transition metal compounds
Transition metals
Density functional theory simulations
Electronic.structure
Gaussian process regression
High-throughput
Inorganics
Kernel function
Robust modeling
Spin state
Structure property
Transition-metal complex
Improving terrestrial evapotranspiration estimation across China during 2000-2018 with machine learning methods
期刊论文
JOURNAL OF HYDROLOGY, 2021, 卷号: 600, 页码: 18
作者:
Yin, Lichang
;
Tao, Fulu
;
Chen, Yi
;
Liu, Fengshan
;
Hu, Jian
收藏
  |  
浏览/下载:55/0
  |  
提交时间:2021/11/05
Evapotranspiration
Machine learning
process-based ET
ET integration
China
Gaussian process regression
A comparative study of Gaussian process regression with other three machine learning approaches in the performance prediction of centrifugal pump
期刊论文
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE, 2021, 卷号: 236, 期号: 8, 页码: 3938-3949
作者:
Zhao, Xutao
;
Zhang, Desheng
;
Zhang, Renhui
;
Xu, Bin
收藏
  |  
浏览/下载:9/0
  |  
提交时间:2022/03/01
Centrifugal pump
impeller parameters
performance prediction
machine learning
Gaussian process regression
A multiple extended target multi-Bernouli filter based on Gaussian process regression model
期刊论文
Kongzhi Lilun Yu Yingyong/Control Theory and Applications, 2020, 卷号: 37, 期号: 9, 页码: 1931-1943
作者:
Chen, Hui
;
Li, Guo-Cai
;
Han, Chong-Zhao
;
Du, Jin-Rui
收藏
  |  
浏览/下载:19/0
  |  
提交时间:2020/11/14
Clutter (information theory)
Gaussian distribution
Gaussian noise (electronic)
Kalman filters
Regression analysis
Set theory
Cubature kalman filters
Extended target tracking
Filtering algorithm
Finiteset statistics (FISST)
Gaussian process regression
Gaussian process regression model
Multiple group target tracking
Uncertain environments
Ultrasonic characterization of thermal barrier coatings porosity through BP neural network optimizing Gaussian process regression algorithm
期刊论文
Ultrasonics, 2020, 卷号: 100, 页码: 105981
作者:
Ma Zhiyuan
;
Zhang Wei
;
Luo Zhongbing
;
Sun Xu
;
Li Zongyi
收藏
  |  
浏览/下载:34/0
  |  
提交时间:2019/12/02
BP neural network,Gaussian process regression,Porosity,Thermal barrier coating,Ultrasonic
Determination of influential parameters for prediction of total sediment loads in mountain rivers using kernel-based approaches
期刊论文
JOURNAL OF MOUNTAIN SCIENCE, 2020, 卷号: 17, 期号: 2, 页码: 480-491
作者:
Roushangar, Kiyoumars
;
Shahnazi, Saman
收藏
  |  
浏览/下载:13/0
  |  
提交时间:2020/11/11
Total sediment loads
Support vector machine
Gaussian process regression
Kernel extreme learning machine
Mountain Rivers
Wind speed prediction method using Shared Weight Long Short-Term Memory Network and Gaussian Process Regression
期刊论文
APPLIED ENERGY, 2019, 卷号: 247, 页码: 270-284
作者:
Zhang, Zhendong
;
Ye, Lei
;
Qin, Hui
;
Liu, Yongqi
;
Wang, Chao
收藏
  |  
浏览/下载:7/0
  |  
提交时间:2019/12/02
Wind speed prediction
Long Short-Term Memory Network
Gaussian Process Regression
Shared weight
Forecast uncertainty
Fast example searching for input-adaptive data-driven dehazing with Gaussian process regression
期刊论文
VISUAL COMPUTER, 2019, 卷号: 35, 页码: 565-577
作者:
Fan, Xin
;
Tang, Xianxuan
;
Hou, Minjun
;
Luo, Zhongxuan
收藏
  |  
浏览/下载:2/0
  |  
提交时间:2019/12/02
Image dehazing
Example searching
Gaussian process regression
Input adaptive
Interpolation of wind pressures using Gaussian process regression
期刊论文
JOURNAL OF WIND ENGINEERING AND INDUSTRIAL AERODYNAMICS, 2019, 卷号: 188, 页码: 30-42
作者:
Ma, Xingliang
;
Xu, Fuyou
;
Chen, Bo
收藏
  |  
浏览/下载:4/0
  |  
提交时间:2019/12/02
Wind pressure interpolation
Machine learning
Gaussian process regression
Low-rise building
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