Neural Network Applied to Telescope Pointing Inaccuracy Model
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06. Juli 2024
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Online veröffentlicht: 06. Juli 2024
Seitenbereich: 55 - 63
Eingereicht: 07. Feb. 2024
Akzeptiert: 17. Juni 2024
DOI: https://doi.org/10.2478/arsa-2024-0004
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© 2024 Vitaliy Zhaborovskyy et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
Figure 1.
Figure 2.
Figure 3.
General structure of the TIM network, each row is a layer_ Total number of trainable parameters is 19,762_
| Layer type | Number of neurons | Number of parameters | Activation |
|---|---|---|---|
| InputLayer | 4 | 0 | None |
| Dense | 64 | 320 | tanh |
| Dense | 64 | 4160 | relu |
| Dense | 64 | 4160 | relu |
| Dense | 64 | 4160 | relu |
| Dense | 32 | 2080 | relu |
| Dense | 32 | 1056 | relu |
| Dense | 32 | 1056 | relu |
| Dense | 32 | 1056 | relu |
| Dense | 16 | 528 | relu |
| Dense | 16 | 272 | relu |
| Dense | 16 | 272 | relu |
| Dense | 16 | 272 | relu |
| Dense | 8 | 136 | relu |
| Dense | 8 | 72 | relu |
| Dense | 8 | 72 | relu |
| Dense | 8 | 72 | relu |
| Dense | 2 | 18 | sigmoid |