基于改进PSO优化SVM网络的数控机床故障分类器设计

SVM Network Based on Improved PSO of Nc Machine Fault Classifier Design

为实现小样本下数控机床故障分类器的设计,通过分析故障分类器构造的基本原理,采用支持向量机(SVM)的神经网络实现,然后用改进的粒子群算法(PSO)对SVM的参数进行优化,改进的PSO算法主要采取了团体互助优化策略,系统分别采用了训练样本和测试样本,并用BP神经网络算法和PSOSVM神经网络算法进行测试,通过对比测试说明改进PSO算法的优越性。

For small sample of the design of the NC machine fault classifier, the basic principle of fault classifier structure, was illustrated and using the neural network implementation of support vector machine （SVM）, and used the improved particle swarm （PSO） algorithm to opti- mize parameters of SVM, the improved PSO algorithm mainly adopted association cooperation optimization strategy, system adopted the training sample and test sample respectively, and the BP neural network algorithm and PSOSVM neural network algorithm, through the con- trast test shows that improved PSO algorithm is good.