激光发生器中的数据分类数学模型设计

Mathematical model design of data classification in laser generator

针对激光发生器中分类方法的收敛性较差的问题。本文依据随机森林法,对激光发生器中的数据分类数学模型进行设计,利用统计去噪法对激光发生器中的数据进行去噪,对数据点密度参数特征,在设定阈值范围内部还是外部进行判断,并将范围外部的数据点确定为噪声数据点去除,为高精度的数据分类奠定基础;依据3D-HT特征提取法对激光发生器中的数据特征进行提取,通过激光发生器中的数据原本结构,迅速恢复全部数据的结构,和数据邻近关系,以估计各数据点法矢特征向量;利用前向法对所得特征向量进行筛选,获得特征空间,以增强分类方法的收敛性;根据自顶而下的贪婪法,使得决策树各内部结点能够选取数据分类最佳的特征属性,并将达到本结点的数据区分为两类或者是多类,对这个过程进行重复,一直到可以精确地将全部的训练数据聚类完毕。实验表明,所提方法提高了激光发生器中数据分类的精度和收敛性,相比当前方法具有的一定的优越性。

Aiming at the poor convergence for the classification method in the laser generator,the mathematical model of the data classification in the laser generator is designed based on random forest method. The data in the laser generator is denoised by statistical denoising method. Judge the data point density parameter characteristics whether it’s inside or outside within the set threshold range and the data points outside the range are determined as noise data point removal,which lays a foundation for high-precision data classification. The data features in the laser generator are extracted according to the 3D-HT feature extraction method. The original structure of the data quickly restores the structure of all data and the data neighbor relationship to estimate the normal vector of each data point. The forward feature method is used to filter the obtained feature vectors to obtain the feature space to enhance the convergence of the classification method. The top-down greedy method enables the internal nodes of the decision tree to select the best feature attributes of the data classification,and divides the data reaching the node into two or more categories,repeating this process until all training data can be accurately clustered. Experiments show that the proposed method improves the accuracy and convergence of data classification in the laser generator,and has certain advantages over the current method.