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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.18084 |
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| _version_ | 1866929362121523200 |
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| author | Origer, Sebastien Izzo, Dario |
| author_facet | Origer, Sebastien Izzo, Dario |
| contents | Inspired by the versatility of sinusoidal representation networks (SIRENs), we present a modified Guidance & Control Networks (G&CNETs) variant using periodic activation functions in the hidden layers. We demonstrate that the resulting G&CNETs train faster and achieve a lower overall training error on three different control scenarios on which G&CNETs have been tested previously. A preliminary analysis is presented in an attempt to explain the superior performance of the SIREN architecture for the particular types of tasks that G&CNETs excel on. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_18084 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Guidance and Control Networks with Periodic Activation Functions Origer, Sebastien Izzo, Dario Machine Learning Inspired by the versatility of sinusoidal representation networks (SIRENs), we present a modified Guidance & Control Networks (G&CNETs) variant using periodic activation functions in the hidden layers. We demonstrate that the resulting G&CNETs train faster and achieve a lower overall training error on three different control scenarios on which G&CNETs have been tested previously. A preliminary analysis is presented in an attempt to explain the superior performance of the SIREN architecture for the particular types of tasks that G&CNETs excel on. |
| title | Guidance and Control Networks with Periodic Activation Functions |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.18084 |