Full
Outline
To address the issues of limited nonlinear modeling capability and low prediction accuracy in single-model satellite clock bias prediction, a combined prediction model integrating long short-term memory (LSTM) neural network and random forest (RF) is proposed. This model fully leverages LSTM's ability to deeply mine the temporal characteristics of clock biases, while utilizing the ensemble learning mechanism of Random Forest to non-linearly compensate for the prediction residuals of LSTM, thereby achieving a significant improvement in prediction accuracy. Experiments demonstrate that, compared to the single LSTM model, the LSTM-RF combined model enhances the prediction accuracy for GPS, BDS, Galileo, and GLONASS satellites by 74.4% to 86.8%, 27.5% to 32.0%, 80.5% to 82.7%, and 0 to 18.2%, respectively, in short-term, medium-term, and long-term predictions. This verifies the feasibility and universality of the LSTM-RF combined model for predicting nonlinear clock bias sequences.
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| 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 |
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