【摘要】 由于边坡失稳是一个循序渐进的过程，利用时间序列分析的方法来预测边坡未来变形，有利于实现边坡的稳定性评价。相比于传统的时间序列分析方法，多簇回声状态网络(MCESN)采用动态储备池将输入信号转换为高维状态向量，选择一组最优的状态向量来表示与任务相关的输入动态。为了验证模型的有效性，采用差分整合移动平均自回归模型(ARIMA)、回归支持向量机(SVM)、长短期记忆网络(LSTM)和传统的回声状态网络(ESN)以及MCESN对三峡船闸高边坡位移进行建模与分析，通过对比均方根误差(RMSE)和复相关系数，发现MCESN的预测精度和模型泛化能力更好。结果表明，MCESN在边坡变形预测具有良好的应用前景。更多还原

【Abstract】 Because the slope instability is a gradual process,the use of time series analysis method to predict the future deformation of the slope is conducive to the realization of the stability evaluation of the slope. Compared with the traditional time series analysis method,the Multi-Cluster Echo State Network( MCESN) uses a dynamic reservoir to convert the input signal into a high-dimensional state vector,and selects a set of optimal state vectors to represent the input dynamics related to the task. In order to verify the effectiveness of the model,the article uses ARIMA,SVM,LSTM and ESN,as well as MCESN on displacement of High Slope of Three Gorges Ship Lock is modeled and analyzed,and RMSE and the Multi-correlation coefficient are compared. It is found that MCESN has better prediction accuracy and model generalization ability. The results show that MCESN has a good application prospect in slope deformation prediction.更多还原