Prediction and model optimization of shearer memory cutting trajectory based on deep learning

It is of great significance to improve the precision of memory cutting of shearers,which is im-proving the automation of shearer.In view of the low precision of traditional shearer memory cutting and the repeatability of shearer cutting,a cutting path prediction method based on long short-term memory (LSTM) neural network was proposed.In MATLAB,the model was validated with real cutting data.The prediction results show that the deep LSTM has a higher accuracy than other machine learning algorithms,such as support vector regression and gradient boosted regression trees.The mean absolute error,mean absolute percentage error,and root mean square error of deep LSTM are lower than those of support vector regression and gradient boosted regression trees.In order to further improve the ability of the model in multi-step prediction,the scale factor was introduced to improve the structure of LSTM neural network.The scale factor can improve the ability of LSTM to keep memory and alleviating the problem that the error of deep LSTM neural network increases with the increase of prediction step.The experimental results show that the improved prediction model performs better in multi-step prediction.