Memory cutting and optimization of shearer based on K-GRU neural network
Objective Aiming at the inaccurate memory cutting and the low degree of automation of shearer, Methods This paper proposed a shearer memory cutting algorithm based on K-GRU neural network.This algorithm was more suitable for processing long-time sequence data.Combining the algorithm with the memory...
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| Veröffentlicht in: | 河南理工大学学报. 自然科学版 2024-01, Vol.43 (1), p.96 |
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| container_title | 河南理工大学学报. 自然科学版 |
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| creator | An, Weipeng Yan, Penghao Zhang, Wenbo Sun, Xuxu |
| description | Objective Aiming at the inaccurate memory cutting and the low degree of automation of shearer, Methods This paper proposed a shearer memory cutting algorithm based on K-GRU neural network.This algorithm was more suitable for processing long-time sequence data.Combining the algorithm with the memory cutting of shearer can reduce the damage of the drum during the coal mining process and protect the safety of workers' lives.The algorithm introduced the proportional factor K at the input end of the deep gated recurrent unit（GRU）,and used the proportional factor K to show the importance of data at different times and to strengthen the memory of the model for long-time sequence data,thereby improving the accuracy of memory cutting.In the model training stage,the random search algorithm（RS）was used to optimize the hyperparameter selection of the deep K-GRU neural network to speed up the training speed of the model. Results In the experiment,Python was used to complete the construction of the K-GRU model and the opti |
| doi_str_mv | 10.16186/j.cnki.1673-9787.2021090055 |
| format | Article |
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| issn | 1673-9787 |
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| subjects | Accuracy Algorithms Coal mining Cutting Neural networks Optimization Search algorithms |
| title | Memory cutting and optimization of shearer based on K-GRU neural network |
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