基于支持向量机和小波包能量特征的气液两相流流型识别方法

支持向量机（SVM）是一种基于统计学习理论（SLT）的机器学习算法,它能在训练样本很少的情况下得到很好的分类效果,从而为流型识别技术向智能化发展提供了新的途径.该文提出了应用支持向量机和小波包能量特征的流型识别方法.将压差波动信号小波包分解后的频带能量作为支持向量机的输入特征向量,并对水平管内空气-水两相流的流型进行了识别.试验结果表明：与BP神经网络相比,采用支持向量机进行流型识别可以获得更高的识别率,表明该方法是有效、可行的.

IDENTIFICATION METHOD OF GAS-LIQUID TWO-PHASE FLOW REGIME BASED ON SUPPORT VECTOR MACHINE AND WAVELET PACKET ENERGE FEATURE

The support vector machine (SVM) is a machine-learning algorithm base on the statistical learning theory (SLT), which has desirable classification ability even if with fewer samples. SVM provides us with a new method to develop the intelligent flow regimes identification. A novel method of flow regime identification based on support vector machine and wavelet packet decomposition is proposed in this paper. The energy of different frequency bands after wavelet packet decomposition constitutes the input vectors of support vector machine as feature vectors. The result of air-water two-phase flow regimes identification in horizontal pipe by using SVM is compared with that by using BP neural network, which shows that the SVM has higher identification accuracy than BP neural network. The results prove the method is efficient and feasible.