基于神经网络理论的轴承内孔加工精度的预测研究  

Study on predictive about manufacturing precision of the bore of bearing inter rings based on neural network theory

作　　者：亓四华[1] 费业泰[2] 孙健[2] 

机构地区：[1]中国科学技术大学,安徽合肥230052 [2]合肥工业大学,安徽合肥230009

出　　处：《组合机床与自动化加工技术》2003年第8期8-9,共2页Modular Machine Tool & Automatic Manufacturing Technique

基　　金：国家自然科学基金重点资助项目 (编号 :597351 2 0 )

摘　　要：针对神经网络的特点 ,探讨了神经网络对非线性时间序列预测的应用。利用神经网络建立了轴承内圈内径磨削过程尺寸精度的数学模型。比较了神经网络模型和灰色系统模型对磨削过程尺寸精度的预测效果 ,得到神经网络模型的预测精度高于灰色系统模型。Based on specific features of the neural network, this paper is concerned with its application to prediction of nonlinear time sequence. A mathematical model of the dynamic distribution of dimension error in bore of bearing internal grinding process is established by means of the neural network theory. And the calculated precision between gray system model GM(1,1) and BPNN model are compared. The result shows that the calculated precision is higher for BPNN model then that for the gray system model.

关 键 词：轴承内孔 加工精度 神经网络 预测 数学模型 灰色系统 磨削 

分 类 号：TH161[机械工程—机械制造及自动化] TP183[自动化与计算机技术—控制理论与控制工程]