Two-component flow identification based on neural network and 8-electrode capacitance sensor

被引：0

作者：

Yan, H ^{[1
]}

Liu, YH ^{[1
]}

Liu, CT ^{[1
]}

机构：

[1] Shenyang Univ Technol, Coll 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：

two-component flow pattern identification; neural network; 8-electrode ECT sensor;

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：

A new method of two-component flow pattern identification based on neural network and an 8-electrode ECT sensor is proposed in this paper The general idea relies on the finding of flow pattern information hidden in ECT sensor outputs by means of a trained neural network. To obtain good identification effect, the neural network inputs are not simple ECT sensor outputs, but the feature parameters extracted from the ECT sensor outputs. 8 feature parameters are extracted and a. two-rank competitive neural network using the feature parameters as inputs is set up, trained and used to identify flow pattern on-line. Simulation results show that the proposed method has good identification precision and fast identification speed.

引用

页码：385 / 388

页数：4