基于PSO的支持向量机多元控制图均值偏移诊断模型

为了诊断多元控制图发出的报警信号是由哪一个或者哪些变量组合发生均值偏移引起的，提出了基于粒子群优化（PSO）算法的支持向量机（SVM）多元控制图均值偏移诊断模型．模型中使用丁。控制图对多元过程进行控制，在假设过程方差．协方差矩阵保持不变的前提下，根据不同的均值偏移模式，产生SVM训练数据集和测试数据集，用Ps0对SVM的参数进行优化，最终得到优化的SVM模型．结果表明，基于粒子群优化算法的支持向量机模型（SVM．PSO）比基于SVM和基于神经网络（ANN）模型的分类能力更强，分类准确率超过85％．

Support Vector Machine Based on Particle Swarm Optimization for Monitoring Mean Shift Signals in Multivariate Control Charts

Abstract： To detect the variable shifts causing out-of-control signals in multivariate control chart, this paper proposes a model of support vector machine （SVM） monitoring the mean shifts of multivariate control charts based on particle swarm optimization （PSO） algorithm. Under the assumptions that the variance matrix is constant, 7a control chart is used to monitor the multivariate process. Based on different mean shift pattems, the sample data are generated. Finally, the optimized model is attained after the parameters of SVM are optimized using PSO. The simulation comparative studies show that the classification ability of the proposed SVM-PSO method outperforms that of the SVM based model and artificial neural network （ANN） based model. The classification rate of the proposed method is higher than 85%. Keywords： multivariate control chart; mean shift diagnosis; particle swarm optimization algorithm; support vector machine