An algorithm for strong wind speed warning of high-speed train based on BIRCH clustering and re-current neural network
To solve the problem that non periodicity and randomness of data in predicting the near wind speed of high-speed trains,a near wind speed prediction and warning system based on BIRCH clustering and LSTM algorithm is constructed.The system firstly performs cross validation of historical data,then uses BIRCH for online incremental clustering.Finally,based on the clustering results,the data closest to the current predicted time series is selected for LSTM rolling training and predicted to obtain the prediction and warning results.Therefore,the method has characteristics of not relying on numerical prediction products and adapting to random data.Experiments show that two algorithms of system are run in parallel,with high efficiency and good prediction results.Thus,it is a new way to solve the problem of strong wind speed pre-diction.
high speed railwaywind speedprediction and warningclusteringrecurrent neural network
万方数据
维普
