中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
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CAS IR Grid
机构
计算技术研究所 [1]
长春光学精密机械与物... [1]
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OAI收割 [2]
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会议论文 [1]
期刊论文 [1]
发表日期
2019 [1]
2010 [1]
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Trend-Smooth: Accelerate Asynchronous SGD by Smoothing Parameters Using Parameter Trends
期刊论文
OAI收割
IEEE ACCESS, 2019, 卷号: 7, 页码: 156848-156859
作者:
Cui, Guoxin
;
Guo, Jiafeng
;
Fan, Yixing
;
Lan, Yanyan
;
Cheng, Xueqi
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收藏
  |  
浏览/下载:16/0
  |  
提交时间:2020/12/10
Training
Market research
Acceleration
Convergence
Servers
Stochastic processes
Machine learning
Parameter trend
asynchronous SGD
accelerate training
The costs prediction of AOD furnace based on improved RBF neural network (EI CONFERENCE)
会议论文
OAI收割
2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering, CMCE 2010, August 24, 2010 - August 26, 2010, Changchun, China
Na T.
;
Zhang D.-J.
;
Hui L.
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浏览/下载:16/0
  |  
提交时间:2013/03/25
In order to predict the cost
a model of cost prediction was set up based on adaptive hierarchical genetic algorithm and RBF neural network. Hierarchical genetic algorithm could optimize the topology and the parameters simultaneously. Compared with simple genetic algorithm
it has more efficiency in not only accelerating and stabilizing the parameters training but also determining the structure of the network. Adaptive crossover and mutation probability could accelerate the speed and avoid prematurity. The model was tested by five samples. The results showed that the prediction model has high prediction accuracy
which indicated that it was applicable to predict the cost by the model. 2010 IEEE.