重庆理工大学学报：自然科学2012，Vol.26Issue(2)：55-59,5.
基于改进神经网络的少齿差行星齿轮参数优化设计
Optimal Design of Parameter for Few-tooth-difference Planetary Gear Transmission Based on Improved Neural Network
摘要
Abstract
This paper, on the basis of analyzing few - tooth - difference planetary gear structure and according to its transmission characteristics and design requirements, builds a reliability mathematical model based on ambiguity reliability analysis through ambiguity reliability principle, which transforms the ambiguity design optimization model into the common optimization model. Through this model, the optimal parameters can be selected. And considering the deficiency of the traditional BP neural net- work, this paper designs a new type of improved neural network by combining stimulated annealing and BP neural network. The results showed that the absolute error and relative error through this algo-rithm are smaller. This is a comparatively accurate method for optimization design.关键词
少齿差行星齿轮/BP神经函数/模拟退火/模糊可靠性Key words
few-tooth-difference planetary gear/BP neural network/stimulated annealing/ambiguity reliability分类
机械制造引用本文复制引用
吕俊峰,陈小安,赵孟娜..基于改进神经网络的少齿差行星齿轮参数优化设计[J].重庆理工大学学报：自然科学,2012,26(2):55-59,5.基金项目
基金项目：国家自然科学基金资助项目 （）
