[1]伍建国,孙庆鸿,毛海军,等.基于BP神经网络模型的磨床部件动态灵敏度分析[J].东南大学学报(自然科学版),2002,32(4):601-604.[doi:10.3969/j.issn.1001-0505.2002.04.014]
 Wu Jianguo,Sun Qinghong,Mao Haijun,et al.Dynamic sensitivity analysis of grinder parts based on the BP neural network model[J].Journal of Southeast University (Natural Science Edition),2002,32(4):601-604.[doi:10.3969/j.issn.1001-0505.2002.04.014]
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基于BP神经网络模型的磨床部件动态灵敏度分析()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
32
期数:
2002年第4期
页码:
601-604
栏目:
数学、物理学、力学
出版日期:
2002-07-20

文章信息/Info

Title:
Dynamic sensitivity analysis of grinder parts based on the BP neural network model
作者:
伍建国1 孙庆鸿1 毛海军1 周德廉1郁文凯2 蔡英2
1 (东南大学机械工程系,南京 210096; 2 无锡机床股份有限公司,无锡 210061
Author(s):
Wu Jianguo1 Sun Qinghong1 Mao Haijun1 Zhou Delian1 Yu Wenkai2 Cai Ying2
1 Department of Mechanical Engineering, Southeast University, Nanjing 210096, China
2 Wuxi Machine Tool Ltd.,Wuxi 210061, China
关键词:
BP神经网络 动态设计 灵敏度 采样
Keywords:
BP neural network dynamic design sensitivity sampling
分类号:
TB115
DOI:
10.3969/j.issn.1001-0505.2002.04.014
摘要:
利用ANSYS的APDL语言建立磨床部件的参数化模型,计算出磨床部件的动态特性,快速采样得到BP神经网络模型的学习样本,建立基于BP神经网络的动态分析模型,将磨床部件结构参数与其动态特性之间的关系反映为神经网络模型的网络输入与网络输出之间的数学关系,从而方便地、快速地对磨床部件进行了动态灵敏度分析.结果表明,在BP神经网络模型上进行磨床结构优化要比在有限元模型上方便、快速,该方法特别适用于对大型复杂结构的优化设计计算.
Abstract:
With the help of parametrization model, the dynamic properties of the grinder part were calculated and the train sample of the BP neural network was obtained quickly. The dynamic analysis models based on the neural network are built up. The relationship between dimensions and dynamic properties of the grinder’s part is defined as the mathematic relationship between inputs and outputs of the BP neural network. Thus the analysis of dynamic sensitivity for grinder’s part can be carried out conveniently and quickly. This method is particularly suitable for calculation of design optimization for large and complex parts.

参考文献/References:

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备注/Memo

备注/Memo:
基金项目: 江苏省“九五”重大工业攻关资助项目(GB98002-2).
作者简介: 伍建国(1960—),男,高级访问学者; 孙庆鸿(联系人),男,教授,博士生导师,me205@seu.edu.cn.
更新日期/Last Update: 2002-07-20