| 动态模糊神经网络在变形预测中的应用 |
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| 引用本文： | 肖桂元,刘立龙. 动态模糊神经网络在变形预测中的应用[J]. 桂林工学院学报, 2011, 0(3): 395-398 |
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| 作者姓名： | 肖桂元 刘立龙 |
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| 作者单位： | 同济大学道路与交通工程教育部重点实验室;桂林理工大学土木与建筑工程学院;桂林理工大学广西建筑工程检测与试验重点实验室;桂林理工大学广西空间信息与测绘重点实验室; |
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| 基金项目： | 国家自然科学基金项目(4106400151108110) |
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| 摘 要： | 为了得到更好的桥梁墩台沉降变形预测精度,减少工程监测实践的误差,分别介绍了基于扩展径向基函数神经网络（RBFNN）与动态模糊神经网络（DFNN）的学习算法和参数的确定方法。选取某一桥梁沉降监测数据分别进行基于扩展径向基函数神经网络与动态模糊神经网络的自适应学习训练,进行桥梁墩台沉降变形预测。实例分析结果表明,径向基函数神经网络预测误差达到0.15 mm,而动态模糊神经网络预测误差达到0.07 mm,显然动态模糊神经网络具有更高的预测精度,从而证实了动态模糊技术与神经网络相结合的自适应学习训练过程的优越性。
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| 关 键 词： | 动态模糊神经网络 径向基函数神经网络 变形预测 |
Application of Dynamic Fuzzy Neural Network to Deformation Prediction |
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| XIAO Gui-yuan,,LIU Li-long. Application of Dynamic Fuzzy Neural Network to Deformation Prediction[J]. Journal of Guilin University of Technology, 2011, 0(3): 395-398 |
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| Authors: | XIAO Gui-yuan LIU Li-long |
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| Affiliation: | XIAO Gui-yuan1,2,LIU Li-long2(1.Key Laboratory of Road and Traffic Engineering,Ministry of Education,Tongji University,Shanghai 201804,China,2.a.College of Civil Engineering,b.Guangxi Key Laboratory of Detection and Test of Architectural Engineering,c.Guangxi Key Laboratory for Spatial Information and Geomatics,Guilin University of Technology,Guilin 541004,China) |
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| 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 ... |
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| Keywords: | dynamic fuzzy neural network radial basis function neural network deformation prediction |
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