Full
Outline
To accurately predict the state-of-charge (SOC) of electric loader batteries, a prediction model combining convolutional neural networks (CNN), bi-directional long short-term memory (BiLSTM) network and attention mechanism was proposed. First, the Pearson correlation coefficient analysis method was used to filter the input features; second, the spatial features were extracted by CNN; then the extracted feature sequences were input into the bi-directional LSTM layer for time series modeling, in addition, the attention mechanism was fused to allocate weights, so as to enhance the important features; finally, the prediction results were output by the fully-connected layer. Experimental results show that the prediction accuracy of the CNN-BiLSTM-Attention model reached 99.97%, and the prediction error was within 1.00%. Compared with models such as the CNN-LSTM-Attention model, this model had higher prediction accuracy and smaller error, indicating certain superiority and reliability.
27
3
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
关闭全屏
No. 86 Xueyuan South Road, Haidian District, Beijing
010-62662699(Operation and Technical Support)
qkjq@cast.org.cn
Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House
