桂林理工大学学报2011，Vol.31Issue(3)：395-398,4.
动态模糊神经网络在变形预测中的应用
Application of Dynamic Fuzzy Neural Network to Deformation Prediction
摘要
Abstract
To get better prediction precision in settlement and deformation of the bridge piers and reduce errors in project monitoring practices, the learning algorithm and determination of network parameters of dynamic fuzzy neural network (DFNN) based on extended radial basis function neural networks (RBFNN) are introduced. In the selection of subsidence monitoring data from a bridge for the adaptive learning and training based on RBFNN and DFNN, the experimental results show that the prediction error of RBFNN is about 0. 15 mm, while the DFNN is about 0. 07 mm. The prediction precision of DFNN is better than RBFNN. Thus the advantages of dynamic fuzzy technology and neural network are confirmed in combining adaptive learning and training process.关键词
动态模糊神经网络/径向基函数神经网络/变形预测Key words
dynamic fuzzy neural network/ radial basis function neural network/ deformation prediction分类
交通工程引用本文复制引用
肖桂元,刘立龙..动态模糊神经网络在变形预测中的应用[J].桂林理工大学学报,2011,31(3):395-398,4.基金项目
国家自然科学基金项目(41064001 （）
51108110) （）
