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Application of dynamic fuzzy neural network to deformation prediction

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Authors:Xiao Guiyuan; Liu Lilong
Author Affiliations:Primary:
Tongji University, Shanghai, China
Guilin University of Technology, China
Volume Title:Guilin Ligong Daxue Xuebao Journal of Guilin University of Technology
Source:Guilin Ligong Daxue Xuebao = Journal of Guilin University of Technology, 31(3), p.395-398. Publisher: Guilin University of Technology, Guilin, China. ISSN: 1674-9057
Publication Date:2011
Note:In Chinese with English summary. 13 refs.; illus., incl. 2 tables
Summary: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.
Subjects:Applications; Bridges; Deformation; Dynamics; Foundations; Fuzzy logic; Neural networks; Numerical models; Prediction; Settlement; Simulation
Record ID:710898-13
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