Full metadata record
| DC pole | Hodnota | Jazyk |
|---|---|---|
| dc.contributor.author | Král, Ladislav | |
| dc.contributor.author | Punčochář, Ivo | |
| dc.date.accessioned | 2023-01-02T11:00:09Z | - |
| dc.date.available | 2023-01-02T11:00:09Z | - |
| dc.date.issued | 2022 | |
| dc.identifier.citation | KRÁL, L. PUNČOCHÁŘ, I. Policy search for active fault diagnosis with partially observable state. International Journal of Adaptive Control and Signal Processing, 2022, roč. 36, č. 9, s. 2190-2216. ISSN: 0890-6327 | cs |
| dc.identifier.issn | 0890-6327 | |
| dc.identifier.uri | 2-s2.0-85131886551 | |
| dc.identifier.uri | http://hdl.handle.net/11025/50799 | |
| dc.format | 27 s. | cs |
| dc.format.mimetype | application/pdf | |
| dc.language.iso | en | en |
| dc.publisher | Wiley | en |
| dc.relation.ispartofseries | International Journal of Adaptive Control and Signal Processing | en |
| dc.rights | Plný text je přístupný v rámci univerzity přihlášeným uživatelům. | cs |
| dc.rights | © Wiley | en |
| dc.title | Policy search for active fault diagnosis with partially observable state | en |
| dc.type | článek | cs |
| dc.type | article | en |
| dc.rights.access | restrictedAccess | en |
| dc.type.version | publishedVersion | en |
| dc.description.abstract-translated | The article deals with a novel design of an active fault detector (AFD) for a nonlinear stochastic system with a partially observable state. The imperfect state information problem is converted to a perfect state information problem using a state estimator. Subsequently, the problem is decomposed into separate tasks of an optimal fault detector design and an approximate input generator design using a dynamic programming technique. While the former task is straightforward, the latter represents a nonlinear functional optimization problem. The input generator is approximated by a multi-layer perceptron neural network, and its unknown parameters are found using the policy search method. Effectiveness of the proposed AFD design is demonstrated numerically on a pendulum system and a heating/cooling system. | en |
| dc.subject.translated | approximate dynamic programming | en |
| dc.subject.translated | fault detection | en |
| dc.subject.translated | neural networks | en |
| dc.subject.translated | reinforcement learning | en |
| dc.subject.translated | state estimation | en |
| dc.identifier.doi | 10.1002/acs.3456 | |
| dc.type.status | Peer-reviewed | en |
| dc.identifier.document-number | 811389500001 | |
| dc.identifier.obd | 43936811 | |
| dc.project.ID | GA18-08531S/Aktivní přístup k detekci poruch ve stochastických rozlehlých systémech | cs |
| dc.project.ID | SGS-2022-022/Rozvoj a využití kybernetických systémů identifikace, diagnostiky a řízení 5 | cs |
| Vyskytuje se v kolekcích: | Články / Articles (NTIS) OBD | |
Soubory připojené k záznamu:
| Soubor | Velikost | Formát | |
|---|---|---|---|
| article_IJACSP22_KrPu.pdf | 2,49 MB | Adobe PDF | Zobrazit/otevřít Vyžádat kopii |
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