桥梁水平位移混沌特征识别与神经网络预测研究

跨海大桥系统受外界影响扰动，其变形伴有混沌现象发生。对桥梁变形监测数据实现了混沌识别，运用C-C法计算时间序列的延迟时间，用G-P方法求得最佳嵌入维数，通过求取的时间延迟和最佳嵌入维数对桥梁变形监测数据进行相空间重构，为混沌时间序列预测模型的建立奠定基础；基于RBF神经网络建立混沌时间序列预测模型，对实测数据进行桥梁变形水平位移预测，并与基于最大Lyapunov指数混沌时间序列预测结果以及实测数据进行对比分析。结果表明，基于RBF神经网络建立的混沌时间序列预测模型的预测结果比基于最大Lyapunov指数混沌时间序列预测模型的预测结果要好，且短期预测效果好。

Chaotic Characteristics Recognition and Neural Network Prediction of Bridge Horizontal Displacement Analysis

The cross-sea bridge system is disturbed by external influences, and its deformation is accompanied by chaos. Chaotic identification in bridge deformation monitoring data is realized, delay time of time series is calculated in C-C method, the G-P method is used to obtain the best embedding dimension. Bridge deformation monitoring data is compared with the obtained time delay. Spatial reconstruction lays the foundation for the establishment of the chaotic time series prediction model; the chaotic time series prediction model is established based on the RBF neural network, the horizontal displacement of the bridge deformation is predicted from the measured data, and we conduct comparative analysis of the prediction results of the chaotic time series based on the largest Lyapunov exponent and the measured data. The results shows that the prediction results of chaotic time series based on RBF neural network are better than those of chaotic time series based on maximum Lyapunov index; short-term prediction has good effect.