混沌理论支持下的桥梁变形监测研究
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摘要
针对桥墩的非线性下沉问题,引入了混沌理论。采用改进的C-C算法计算时间序列的时间延迟τ,采用改进的G-P算法计算最佳嵌入维数m,进行相空间重构,并与传统算法对比抗干扰性,计算效率等得到了改善,运用Lvyapunov指数判别该时间序列的混沌特性;最后根据所求参数建立加权一阶局域预计模型和RBF神经网络混沌预计模型,分别对观测数据进行预计分析,将混沌时间预测结果与指数平滑法预测结果进行对比分析。得出混沌时间预测精度高于指数平滑法预测精度,RBF神经网络混沌预计模型的预计精度最高,证明混沌时间序列预计精度可靠,能够实时对桥身变形进行监测,避免灾害的发生。
Aiming at the problem of pier nonlinear sinking,chaos theory is introduced. The reconstructed by the improved C-C and the G-P algorithm of time series,compared with traditional algorithms,the anti-interference and computational efficiency are improved. The maximum Lvyapunov exponent is obtained to determine whether there is chaos in time series. Finally,a weighted first-order local prediction model and a RBF neural network chaotic prediction model are established according to the obtained parameters to respectively predict and analyze the observed data. The chaotic time prediction results are compared with those of exponential smoothing method.The prediction precision of chaotic time is higher than that of exponential smooth method,and the predicted precision of chaotic model of RBF neural network is the highest,which proves that the predicted precision of chaotic time series is reliable,and can monitor the deformation of the bridge body in real time to avoid disasters.
Aiming at the problem of pier nonlinear sinking,chaos theory is introduced. The reconstructed by the improved C-C and the G-P algorithm of time series,compared with traditional algorithms,the anti-interference and computational efficiency are improved. The maximum Lvyapunov exponent is obtained to determine whether there is chaos in time series. Finally,a weighted first-order local prediction model and a RBF neural network chaotic prediction model are established according to the obtained parameters to respectively predict and analyze the observed data. The chaotic time prediction results are compared with those of exponential smoothing method.The prediction precision of chaotic time is higher than that of exponential smooth method,and the predicted precision of chaotic model of RBF neural network is the highest,which proves that the predicted precision of chaotic time series is reliable,and can monitor the deformation of the bridge body in real time to avoid disasters.
引文
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[4]王新迎,韩敏.多元混沌时间序列的多核极端学习机建模预测[J].物理学报,2015,64(7):37-143.
[5]栾元重,栾亨宣,马德鹏,等.桥梁变形数据小波去噪与混沌预测[J].大地测量与地球动力学,2013,33(5):133-135,139.
[6]王建波,栾元重,许君一,等.小波分析桥梁变形监测数据处理[J].测绘科学,2012,37(3):79-81.
[7]卢天秀.关于几类系统混沌性的研究[D].成都:电子科技大学,2013:1-7.
[8]SU L Y,SUN H H,WANG J,et al.Detection and estimation of weak pulse signal in chaotic background noise[J].Acta Physica Sinica,2017,66(9):23-36.
[9]吕金虎,陆君安,陈士华.混沌时间序列分析及其应用[M].武汉:武汉大学出版社,2005:57-85.
[10]GRASSBERGER P,PROCACCIA I.Measuring the strangeness of strange attractors[M].New York:Springer,2004:189-208.
[11]李海波.混沌时间序列预测应用研究[D].合肥:中国科学技术大学,2009:19-25.
[12]刘利强.基于IABC-RBF算法和小波分析的瓦斯时间序列优化预测[D].辽宁:工程技术大学,2015:33-39.
[13]ZHANG R D,TAO J L,LU R Q,et al.Decoupled ARXand RBF neural network modeling using PCA and GAoptimization for nonlinear distributed parameter systems[J].IEEE Transactions on Neural Networks and Learning Systems,2018,29(2):457-469.
[14]雷宇.基于RBF神经网络的电梯故障诊断方法研究[D].武汉:武汉理工大学,2015:12-20.
[15]李文华,马思宁,沈培根,等.振动条件下铁路继电器寿命预测研究[J].电气工程学报,2017,12(7):8-15.
[16]徐国宾,赵丽娜.基于多元时间序列的河流混沌特性研究[J].泥沙研究,2017,42(3):7-13.
[17]王超,朱明,赵元棣.基于改进加权一阶局域法的空中交通流量预测模型[J].西安交通大学学报,2018,53(1):206-213.
[18]莫小琴,李钟慎.混沌时间序列的LSSVM预测方法[J].华侨大学学报(自然科学版),2014,35(4):373-377.