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Robust Space-Filling Input Design via Stochastic Optimization — arXiv:2608.13360

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.13360
License (as archived): CC0-1.0
Archived: 2026-10-19T05:13:43.215Z
SHA-256 of the archived text: a3047f4a81dba10ed1375f10bf27cd29d6e57959f2441e09bada23f3d81d3a37
Stable URL of this page: https://metraipolis.com/documento/dc_7bca9de30ffc

How to cite

Robust Space-Filling Input Design via Stochastic Optimization — arXiv:2608.13360 — arXiv (en), https://arxiv.org/abs/2608.13360 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7bca9de30ffc (Metraipolis archive dc_7bca9de30ffc, sha256 a3047f4a81dba10ed1375f10bf27cd29d6e57959f2441e09bada23f3d81d3a37)

BibTeX

@misc{dc7bca9de30ffc,
  title = {Robust Space-Filling Input Design via Stochastic Optimization — arXiv:2608.13360},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.13360},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7bca9de30ffc; sha256 a3047f4a81dba10ed1375f10bf27cd29d6e57959f2441e09bada23f3d81d3a37},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.13360. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

The space-filling input design approach generates a so-called space-filling dataset in the feature space of the system model. The design method is applicable on a broad class of model structures with wide selection of signals and also incorporates information measures through optimality criteria into the signal design. However, during the signal design, knowledge of a hypothesized model is required. The designed signal can perform far from the optimal if the true system is significantly different from the hypothesized system model. This paper proposes a robust space-filling input design algorithm that can generate a space-filling dataset for an entire class of models. The proposed algorithm takes the expectation of an optimality criterion over the population of the model class, and a stochastic approximation technique is employed to optimize this robust criteria. The efficiency of the proposed algorithm is demonstrated in a simulation example.

Tema degli esploratori: calcolo e infrastrutture (retro-catalogo arXiv: annunci del 2026-08-14 in eess.SY).
Categorie arXiv: Systems and Control (eess.SY).
Pagina del lavoro: https://arxiv.org/abs/2608.13360 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T05:07:23.935Z dalla pagina https://arxiv.org/catchup/eess.SY/2026-08-14?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).