[1]王莉莉,刘洪波,陈德运,等. 自适应与附加动量BP 神经网络的ECT 流型辨识[J].哈尔滨理工大学学报,2018,(01):105-110.[doi:10. 15938 /j. jhust. 2018. 01. 019]
 WANG Li-li,LIU Hong-bo,CHEN De-yun,et al. Identification of Flow Regimes Based on AdaptiveLearning and Additional Momentum BP NeuralNetwork for Electrical Capacitance Tomography[J].哈尔滨理工大学学报,2018,(01):105-110.[doi:10. 15938 /j. jhust. 2018. 01. 019]
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 自适应与附加动量BP 神经网络的ECT 流型辨识()
分享到:

《哈尔滨理工大学学报》[ISSN:1007-2683/CN:23-1404/N]

卷:
期数:
2018年01期
页码:
105-110
栏目:
材料科学与工程
出版日期:
2018-02-25

文章信息/Info

Title:
 Identification of Flow Regimes Based on Adaptive
Learning and Additional Momentum BP Neural
Network for Electrical Capacitance Tomography
文章编号:
1007- 2683( 2018) 01- 0105- 06
作者:
 王莉莉 刘洪波 陈德运 冯其帅
( 哈尔滨理工大学计算机科学与技术学院,黑龙江哈尔滨150080
Author(s):
 WANG Li-li LIU Hong-bo CHEN De-yun FENG Qi-shuai
 ( School of Computer Science and Technology,Harbin University of Science and Technology,Harbin 150080,China)
关键词:
 电容层析成像 流型辨识 BP 神经网络 局部极小值 收敛速度
Keywords:
 electrical capacitance tomography flow regime identification BP neural network local minimumconvergence speed
分类号:
TP391. 9
DOI:
10. 15938 /j. jhust. 2018. 01. 019
文献标志码:
A
摘要:
 传统BP 神经网络是解决电容层析成像系统流型辨识经典的算法,虽然在一些简单问
题上达到了工业实际应用的要求,但如果解决复杂工业问题时就会暴露出很多缺陷。针对传统BP
神经网络算法的不足,为降低误差震荡现象,引入了自适应调节学习速率和附加动量因子。通过输
入电容值进行训练,得到适合流型识别神经网络。仿真实验结果表明,该算法不仅继承传统BP 神
经网络的优点,而且还提高了ECT 系统流型辨识中的收敛速度慢,解决了容易陷入局部极小值的
问题
Abstract:
 Traditional BP neural network is a typical mehtod to solve ECT system of flow pattern identification.
It is applied to the simple problems in industrial applications,but there are many defects in solving complex
industrial problems. In this paper based on the analysis of deficiency of BP neural network,for reducing the error
oscillation,the adaptive learning rate adjustment factor and the additional momentum is introduced. In this method,
the electrical capacitance values are input to train a network to identify the flow patterns. The simulation results
show the algorithm not only inherits the advantages of traditional BP neural network,but also improve slow
convergence and solve being prone to fall into local minimum problems in flow pattern identification of ECT system

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更新日期/Last Update: 2018-05-24