基于支持向量机的磨削参数自适应系统

基于支持向量机的磨削参数决策系统通过多传感器信息融合获得磨削状态信息,用支持向量机分类器对其分类.经建立样本数据、选取核函数及其参数并求解拉格朗日系数,找出支持向量.再求解分类超平面系数,建立训练数据最优决策超平面,并根据样本数据学习.系统按分类学习结果自动选择工艺参数以控制磨削加工质量.

Self-Adaptive System for Grinding Parameter Based on SVM

For self-adaptive system of grinding parameter based on SVM (support vector machine), the information of grinding state is gained through the multi-sensors data fusion method, and the information is classified with the SVM classificatory device. At first, the data set was built up, the kernel function and its parameter was selected, Lagrangian coefficients were solved and the support vectors were founded out. Secondly, the hyper-plane coefficients for classifying were solved, the optimal separating hyper-plane were founded and studied according to stylebook data. Finally, according to the result of classifying study the system automatically selects the technology parameter to control the grinding quality.