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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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: He, Qi (Autor), Wang, Le Yi (Autor), Yin, George G. (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: New York, NY : Springer New York : Imprint: Springer, 2013.
Edición:1st ed. 2013.
Colección:SpringerBriefs in Mathematics,
Temas:
System theory.
Control theory.
Control engineering.
Probabilities.
Systems Theory, Control .
Control and Systems Theory.
Probability Theory.
Acceso en línea:Texto Completo

MARC

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245 1 0 |a System Identification Using Regular and Quantized Observations  |h [electronic resource] :  |b Applications of Large Deviations Principles /  |c by Qi He, Le Yi Wang, George G. Yin. 
250 |a 1st ed. 2013. 
264 1 |a New York, NY :  |b Springer New York :  |b Imprint: Springer,  |c 2013. 
300 |a XII, 95 p. 17 illus., 16 illus. in color.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
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505 0 |a 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. 
520 |a 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. 
650 0 |a System theory. 
650 0 |a Control theory. 
650 0 |a Control engineering. 
650 0 |a Probabilities. 
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650 2 4 |a Probability Theory. 
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700 1 |a Yin, George G.  |e author.  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
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950 |a Mathematics and Statistics (R0) (SpringerNature-43713) 

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