基于粗神经网络和特征提取ECT流型辨识.docVIP

基于粗神经网络和特征提取的ECT流型辨识 刘延东1，李惠强2，何在刚2，刘浩仟2，陈玲2，郑静娜2，郑斯文3 （110179）Flow pattern identification based on feature extraction and Rough Neural Network for Electrical Capacitance Tomography LIU Yan-dong1，LI HUI-qiang2, HE Zai-gang2，LIU Hao-qian2, CHEN Ling2, ZHENG Jing-na2，ZHENG Si-wen3 (1.Light Industry College，Liaoning University, Shenyang Liaoning 110036, China; 2.College of Physics, Liaoning University, Shenyang Liaoning 110036, China; 3.Shenyang Metrology Testing Institution, Shenyang Liaoning 110179, China) Abstract：In response to the inaccuracy of flow pattern identification by Electrical Capacitance Tomography, a new method is put forward that combines rough neural network with feature extraction, to identify the two-phase flow patterns. his method according to the Electrical Capacitance Tomography system and the characteristics of flow pattern to handle capacitance measurements, thus complete the features of various flow pattern extraction; Secondly, the structure of rough neural network to carry on the design, and use the features parameters of typical flow pattern to train the rough neural network,?and then using this rough neural network to identify flow pattern;?Finally, the simulation experiment. The simulation experimental results indicate that this method compared with the traditional BP neural network has high identification accuracy, this also for the research of ECT flow pattern identification provides new ways and means. Key words: Electrical capacitance tomography; Rough Neural Network; feature extraction; flow pattern identification; 1、引言 电容层析成像技术[1-2]是近年发展起来的一项新的检测技术。电容层析成像技术（ECT）因其结构简单、、等优点而具有广阔前景RNN）神经网络和特征值提取的流型辨识方法。 2 流型辨识的基本原理 典型的ECT系统由电容传感器系统、数据采集系统、成像计算机三个基本部分组成[2]。如图1所示。ECT系统的工作原理是：电容传感器阵列由管道周围排列的若干电容极板(通常为8极板或12极板)组成，每两个极板组成一个二端子电容，当管道内的介质分布发生变化时会引起极板组成的电容随之发生变化。根据电容值的变化可以实现管道内部介质分布的重建，从而实现多相流参数的检测。 图1 ECT系统结构图 对于气固两相流体来说，当其经充分发展而形成稳定流态后，管道截面上的介质分布也就随之固定了。典型的两相流流型有层流、环流、核心流、均匀流、满管和空管等，不同的流型在管道截面上均对应不同的介质分布。在ECT系统中，通过电容传感器阵列采集到的电容