成像流型辨识算法

Electrical Capacitance Tomography Identification Algorithm Based on GMM Model

针对电容层析成像反问题流型识别较难的问题,提出了一种基于高斯混合模型的电容层析成像流型辩识算法.在阐述高斯混合模型和EM算法的基础上,结合Kmeans算法,通过训练得到各类流型所对应的高斯混合模型参数,构造分类器实现对五种流型的快速与精准的识别.实验结果表明,该算法与BP神经网络、SVM、决策树识别算法相比,辨识准确率高、识别速度快,为电容层析成像流型辨识算法的研究提供了一个新思路.

To solve the flow pattern identification difficulty in electrical capacitance tomography （ECT）, a gaussian mixture model（GMM） flow pattern identification algorithm for electrical capacitance tomography system is presented. On the basis of Gaussian mixture model（GMM） and the principle of EM algorithm, the Kmeans algorithm should be united in wedlock. Then we get the parameter of gaussian mixture model（GMM） through training of electrical capacitance tomography flow pattern, establish a classifier of gaussian mixture model （GMM） to achieve the goal of faster identification of five flow patterns. The experimental result shows that the algorithm has higher identification accuracy than the algorithm of neural network and support vector machine and decision tree. The algorithm provides a new idea for the research on the electrical capacitance tomography identification algorithm.