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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...
| Clasificación: | Libro Electrónico |
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| Autores principales: | , , |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York, NY :
Springer New York : Imprint: Springer,
2013.
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| Edición: | 1st ed. 2013. |
| Colección: | SpringerBriefs in Mathematics,
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| Temas: | |
| Acceso en línea: | Texto Completo |
| Sumario: | 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 perspective to understand the fundamental relationship between probabilistic errors and resources, which may represent data sizes in computer usage, computational complexity in algorithms, sample sizes in statistical analysis and channel bandwidths in communications. |
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| Descripción Física: | XII, 95 p. 17 illus., 16 illus. in color. online resource. |
| ISBN: | 9781461462927 |
| ISSN: | 2191-8201 |