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- W4387588175 abstract "The Transformer model based on Attention mechanism has the ability of highly parallel computing and capturing the dependency between data, providing a new means for time series prediction. However, while Transformer abandons convolution and recursive deduction in exchange for data association capture capability, it also produces data fragmentation problem. In order to overcome the difficulty of obtaining long time series feature data caused by the above reasons, this paper proposes an improved Attention model based on the fragment cycle mechanism, which can improve the computational efficiency and expand the learning range of long time series feature data. On the other hand, in order to improve the accuracy of information location after the learning range is expanded, this paper locates the information location based on the Long Short Time Memory (LSTM) coding method. The result of data analysis shows that the improved Transformer model based on LSTM has high accuracy for the prediction of pump truck energy consumption data in real environment. The comparison and analysis with the prediction results of LSTM and Transformer models prove the superiority of the method proposed in this paper, and provide a new method for the prediction of pump truck energy consumption." @default.
- W4387588175 created "2023-10-13" @default.
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- W4387588175 date "2023-01-01" @default.
- W4387588175 modified "2023-10-14" @default.
- W4387588175 title "Research on Energy Consumption Prediction of Pump Truck Based on LSTM-Transformer" @default.
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- W4387588175 doi "https://doi.org/10.1007/978-981-99-6501-4_36" @default.
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