Search

Your search keyword '"He, Zhaoshuang"' showing total 19 results
Start Over Author "He, Zhaoshuang" Publication Year Range Last 50 years
19 results on '"He, Zhaoshuang"'

2. An Empirical Study of the Influencing Factors on User Experience for Barrage Video Website — A Case Study of Bilibili

Author

Liu, Weilin, He, Zhaoshuang, Liu, Mengxin, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, and Duffy, Vincent G., editor

Published
2021
Full Text
View/download PDF

10. A Combined Model Based on Neural Networks, LSSVM and Weight Coefficients Optimization for Short-Term Electric Load Forecasting

Author

Li, Caihong, He, Zhaoshuang, Wang, Yachen, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Song, Shaoxu, editor, and Tong, Yongxin, editor

Published
2016
Full Text
View/download PDF

18. A novel combined model based on echo state network – a case study of PM10 and PM2.5 prediction in China.

Author

Zhang, Hairui, Shang, Zhihao, Song, Yanru, He, Zhaoshuang, and Li, Lian

Subjects
FORECASTING, PARTICLE swarm optimization, ENVIRONMENTAL quality, METALLIC oxides, PARTICULATE matter, SIMULATED annealing, ARTIFICIAL neural networks
Abstract

Particulate Matters such as PM10, PM2.5 may contain heavy metal oxides and harmful substances that threaten human health and environmental quality. In this paper, we propose a new combined neural network algorithm which based on Elman, echo state network (ESN) and cascaded BP neural network (CBP) to predict PM10 and PM2.5. In order to further improve the performance of the prediction result, we use the simulated annealing algorithm (SA) to optimize the parameters in the combination method to form the optimal combination model. And particle swarm optimization (PSO) is used to optimize the parameters in ESN. The chemical species in the atmosphere which include SO2, NO, NO2, O3 and CO in Baiyin, Gansu Province of China are used to test and verify the proposed combined method. The experimental results show that the prediction performance of the combined model presented in this paper is indeed superior to other three neural network models. [ABSTRACT FROM AUTHOR]

Published
2020
Full Text
View/download PDF

19. A novel combined model based on echo state network - a case study of PM 10 and PM 2.5 prediction in China.

Author

Zhang H, Shang Z, Song Y, He Z, and Li L

Subjects
China, Humans, Neural Networks, Computer, Particulate Matter, Air Pollutants, Metals, Heavy
Abstract

Particulate Matters such as PM 10 , PM 2.5 may contain heavy metal oxides and harmful substances that threaten human health and environmental quality. In this paper, we propose a new combined neural network algorithm which based on Elman, echo state network (ESN) and cascaded BP neural network (CBP) to predict PM 10 and PM 2.5 . In order to further improve the performance of the prediction result, we use the simulated annealing algorithm (SA) to optimize the parameters in the combination method to form the optimal combination model. And particle swarm optimization (PSO) is used to optimize the parameters in ESN. The chemical species in the atmosphere which include SO 2 , NO, NO 2 , O 3 and CO in Baiyin, Gansu Province of China are used to test and verify the proposed combined method. The experimental results show that the prediction performance of the combined model presented in this paper is indeed superior to other three neural network models.

Published
2020
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results