Application of principal Component Analysis and BP neural Network in Gear Noise Prediction

摘要：

This paper presents an experimental investigation on gear noise for identifying the influence of gear tooth geometrical errors caused by machining on the radiated sound. The prediction model for the gear noise was established based on the principal component analysis (PCA) and BP neural network method. Using the principal components of original variables as the input of network can cut down the dimensions of input,and at the same time eliminate the relativity between variables. Back-propagation neural networks were introduced to describe the relationships between gear tooth geometrical errors and sound pressure level. The results indicate that the model has better predictive capability.

关键词： principal component analysis gear noise neural network prediction

作者: Xu yudong Cheng guangming

作者单位: Institute of Mechanical Science and Engineering Jilin University,Changchun,China

会议类型: 国际会议

会议名称: 2010 2nd International Conference on Computer Engineering and Technology(2010年第二届计算机工程与技术国际会议 ICCET 2010)

会议地点: 成都

会议语种:英文

页码: 3076-3079

在线出版日期: 2010-04-16（万方平台首次上网日期，不代表论文的发表时间）