System identification using regular and quantized observations : applications of large deviations principles
This brief presents characterizations of identification errors under a probabilistic framework when output sensors are binary, quantized, or regular. By considering both space complexity in terms of signal quantization and time complexity with respect to data window sizes, this study provides a new...
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| Những tác giả chính: | , , |
|---|---|
| Định dạng: | Livre numérique |
| Ngôn ngữ: | Anglais |
| Được phát hành: |
New York, NY :
Springer New York
2013.
Cham : Springer Nature |
| Loạt: | SpringerBriefs in Mathematics
|
| Truy cập trực tuyến: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Chú thích: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • System Identification Using Regular and Quantized Observations, Texte imprimé, 9781461462934 • System identification using regular and quantized observations, applications of large deviations principles, by Qi He, Le Yi Wang, G. George Yin., New York, NY, Springer, 2013, SpringerBriefs in Mathematics, 978-1-4614-6291-0 |
Mục lục:
- Introduction and Overview.- System Identification: Formulation.- Large Deviations: An Introduction.- LDP under I.I.D. Noises.- LDP under Mixing Noises.- Applications to Battery Diagnosis.- Applications to Medical Signal Processing.-Applications to Electric Machines
- Remarks and Conclusion
- References
- Index.