| 混沌理论在桥梁变形监测中的应用 |
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| 引用本文： | 黄维腾.混沌理论在桥梁变形监测中的应用[J].地理空间信息,2022,20(2):147-151. |
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| 作者姓名： | 黄维腾 |
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| 作者单位： | 茂名市城规勘察测绘院有限公司,广东 茂名 525000 |
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| 摘 要： | 针对桥梁的非线性下沉问题,引用了混沌理论,首先求取时间序列的两重构参数时间延迟τ和嵌入维数m进行相空间重构;随后进行混沌特性判别,确定该时间序列存在混沌迹象;最后根据所求参数建立加权零阶局域预计模型和RBF神经网络混沌预计模型对观测数据进行预计分析,并与系数为0.9的指数平滑预测模型进行比较,结果显示混沌预计模型值更接近实测值,三者相比RBF神经网络混沌预计模型的预计精度优于另外两者,表明混沌预计模型预测精度满足桥梁变形监测精度需求。
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| 关 键 词： | 参数求取 相空间重构 混沌识别 混沌时间序列 加权零阶局域预测 RBF神经网络混沌预测 |
Application of Chaos Theory in Bridge Deformation Monitoring |
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| HUANG Weiteng.Application of Chaos Theory in Bridge Deformation Monitoring[J].Geospatial Information,2022,20(2):147-151. |
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| Authors: | HUANG Weiteng |
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| Institution: | (Maoming city urban survey and mapping institute co.LTD,Maoming 525000,China) |
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| Abstract: | We introduced chaos theory according to the nonlinear subsidence problem of bridges.Firstly,we obtained the time delay T and em-bedded dimension m of the two time series reconstruction parameters to reconstruct the phase space.Then,we identified chaos characteristics to determine the existence of chaos in the time series.Finally,we established a weighted zero-order local prediction model and a chaotic prediction model based on RBF neural network to predict and analyze the observed data.Compared with the exponential smoothing prediction model with a coefficient of 0.9,the predicted value of the chaotic prediction model is closer to the measured value.The prediction accuracy of the chaotic pre-diction model based on RBF neural network is better than that of the other two models,which shows that the chaotic prediction model can meet the bridge deformation monitoring accuracy requirement. |
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| Keywords: | parameter extraction phase space reconstruction chaotic recognition chaotic time series weighted zero order local prediction cha-otic prediction of RBF neural network |
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