Two-phase flow pattern identification based on neural networks and D-S evidence theory

WU Xin-jie1; LIU Shi2; XU Chao1

doi:10.13873/j.1000-97872009.11.002

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Two-phase flow pattern identification based on neural networks and D-S evidence theory

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- Two-phase flow pattern identification based on neural networks and D-S evidence theory
- Vol. 28, Issue 11, Pages: 43-45(2009)
- 作者机构：
  
  1. 辽宁大学物理学院
  2. 华北电力大学能源与动力工程学院
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-97872009.11.002
  CLC： O359
- Published：2009
- 稿件说明：
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[1]吴新杰,刘石,许超.基于神经网络和D-S证据理论辨识两相流流型[J].传感器与微系统,2009,28(11):43-45.

WU Xin-jie1, LIU Shi2, XU Chao1. Two-phase flow pattern identification based on neural networks and D-S evidence theory[J]. 2009, 28(11): 43-45.
[1]吴新杰,刘石,许超.基于神经网络和D-S证据理论辨识两相流流型[J].传感器与微系统,2009,28(11):43-45. DOI： 10.13873/j.1000-97872009.11.002.

WU Xin-jie1, LIU Shi2, XU Chao1. Two-phase flow pattern identification based on neural networks and D-S evidence theory[J]. 2009, 28(11): 43-45. DOI： 10.13873/j.1000-97872009.11.002.

摘要

提出一种基于电容层析成像（ECT）系统、神经网络和证据理论辨识两相流流型的方法。这种方法采用神经网络与D-S证据理论相结合的方法来辨识两相流流型

并对两相流的几种常见流型进行了辨识。仿真实验结果表明:此种方法在两相流流型辨识中具有较高的判别精度

为两相流流型辨识提供了一种有效的手段。

Abstract

A new method of two-phase flow pattern identification based on electrical capacitance tomography（ECT）system

neural networks and D-S evidence theory is proposed.The identification system is built for identifying a few familiar flow patterns of two-phase flow

by using neural networks and D-S evidence theory.The simulation experimental results show that the method can accurately identification flow patterns of two-phase flow.An effective means is presented for flow patterns identification.

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references

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基于希尔伯特—黄变换与Elman神经网络的气液两相流流型识别方法 [J]. 周云龙,王强,孙斌,张永刚. 中国电机工程学报 . 2007(11)

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D-S证据理论在多传感器数据融合中的应用 [J]. 黄瑛,陶云刚,周洁敏,苏登军. 南京航空航天大学学报 . 1999(02)

两相流参数检测及应用[M]. 浙江大学出版社 , 李海青等编著, 1991

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