基于改进BP神经网络的孔板应力集中系数预测  

Stress Concentration Factors Prediction of Orifice Plate Based on Modified BP Neural Network

作　　者：贾延[1] 刘广君[2] 

机构地区：[1]西北第二民族学院基础部,银川750021 [2]西北第二民族学院材料科学与工程学院,银川750021

出　　处：《煤矿机械》2008年第2期38-40,共3页Coal Mine Machinery

基　　金：西北第二民族学院校级重点资助项目(2006Y046)

摘　　要：在正交试验和无网格法数值计算结果的基础上,针对常用BP算法的不足,采用动量因子与自适应学习速率相结合的改进BP神经网络方法,建立了孔板应力集中系数预测模型。经过计算结果的检验,表明该模型是可行的,对今后孔板应力集中系数预测具有借鉴意义。Based on orthogonal experiment and numerical calculation results of meshless method, in light of deficiency of the normal BP algorithm, a neural network predictive model for the stress concentration factors of orifice plates established on the basis of the improved BP algorithm in combination of momentum factor and self- adaptive learning rate. Tested by results which export from computer, this model is proved to be feasible, and offers reference to the stress concentration factor prediction of orifice plate.

关 键 词：应力集中 BP神经网络 孔板 预测 

分 类 号：TP393[自动化与计算机技术—计算机应用技术] O343[自动化与计算机技术—计算机科学与技术]