EMD与神经网络在气液两相流流型识别中的应用

本文提出了EMD与Elman神经网络相结合的气液两相流流型识别的新方法.将压差波动信号经验模态分解(EMD)后的固有模态函数(IMF)进行分析、提取IMF能量作为Elman神经网络的输入特征向量,对水平管内的气液两相流流型进行识别.实验结果表明:该方法优于BP网络且稳定、识别率高,具有可行性.

APPLIED STUDY OF EMD AND NEURAL NETWORKS ON FLOW REGIME IDENTIFICATION FOR GAS-LIQUID TWO-PHASE FLOW

In this article a flow regime identification method using the EMD combined with Elman neural network was put forward.Firstly the method analyzes the intrinsic mode function (IMF) obtained after the empirical mode decomposition (EMD),then extracts IMF energy as the input feature vectors of the Elman neural network,at last flow regime identification of the gas-liquid two- phase flow in a horizontal pipe can be performed.The experimental results show that this method is superior to BP neural network,and it is not only stable but also higher identification.The results prove the method is feasible.