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湖南大学 [12]

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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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