Determination of flow regimes from raw capacitance tomography data using neural networks

被引：0
|
作者：
Yan, H [1 ]
Liu, CT [1 ]
Liu, YH [1 ]
机构：
[1] Shenyang Univ Technol, Sch Informat Sci & Engn, Shenyang 110023, Peoples R China
来源：
ICEMI'2003: PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE ON ELECTRONIC MEASUREMENT & INSTRUMENTS, VOLS 1-3 | 2003年
关键词：
electrical capacitance tomography; flow-regime identification; back-propagation network; feature parameter extraction;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
An application of electrical capacitance tomography for two-component flow identification without the need for time-consuming image reconstruction and analysis is presented 10 feature parameters are extracted straight from the capacitance measurements and translated into regime information via a back-propagation (BP) network. The extraction of feature parameters, the architecture and the training of the BP network are give. Simulation results show that the new identification method has good precision and fast speed The use of feature parameters and the BP network for flow-regime identification is promising.
引用
收藏
页码：2313 / 2317
页数：5
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