基于径向基函数的变量预测模型模式识别方法

针对变量预测模型模式识别方法中4种数学模型不足以反映特征值之间复杂关系的缺陷.因此,提出了一种基于径向基函数的变量预测模型(VPMRBF)模式识别方法,把提取的特征值输入到VPMRBF分类器中,然后通过训练样本建立反映特征值之间复杂关系的径向基函数预测模型,最后把测试样本的特征值作为径向基函数预测模型的输入,以预测误差平方和为依据完成分类.该方法充分有效地利用并且结合径向基函数和变量预测模式识别方法的优点,实现了故障特征提取到故障识别的全程诊断. 滚动轴承故障诊断实验分析结果表明:与径向基神经网络、支持向量机和变量预测模式识别方法相比,VPMRBF的识别率分别提高了4.75%，1.75%和5.25%. 

Variable predictive model based RBF class discriminate method

Considering that the defect of four kinds of mathematical models can not reflect the complex relationship between the features in variable predictive model based class discriminate method, a variable predictive model based radial basis function (VPMRBF) method was put forward., Firstly, the abstracted features were input into VPMRBF classifier, and then the training samples were used to establish radial basis function models, which could reflect the complex relationship between features; finally, the established radial basis function prediction models were used to predict the features of those test samples, and the sum of squares prediction error could be employed as a basis for necessary classification. Experimental results of roller bearing fault diagnosis showed that the recognition rate of VPMRBF increased by 4.75%, 1.75% and 5.25% respectively, compared with the radial basis function neural network, the support vector machine and variable predictive model based class discrimination method. By making full use of and effectively combining the advantages of radial basis function and variable predictive model based class discrimination method, this realized entire diagnosis from fault feature extraction to fault identification.