Prediction Research of Vault Sink Based on an Improved Neural Network of Wavelet De-noising

摘要：

Vault sink of tunnel contains a lot of random error. In order to eliminate or weaken interference of random error, the measured data was processed by wavelet de-noising that made the data more authenticity in the paper. Aiming at problems such as poor precision and slow convergence about BP neural network prediction, de-noising data was predicted by the improved BP neural networK which compared with traditional BP neural network. Experimental results showed the improved neural network of wavelet de-noising made convergence rate accelerate, accuracy improve, prediction result significantly enhance, it was true to prediction research of vault sinK

关键词： vault sink neural network wavelet de-noising convergence rate prediction research

作者: WANG Zegen LI Fapeng

作者单位: School of Civil & Architecture,Southwest Petroleum University,China,610500

会议类型: 国际会议

会议名称: 2011 International Symposium on Geospatial Information Technology & Disaster Prevention and Reduction(2011地球空间信息与防灾减灾国际学术研讨会)

会议地点: 成都

会议语种:英文

页码: 107-110

在线出版日期: 2011-05-13（万方平台首次上网日期，不代表论文的发表时间）