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?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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Contenuti
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
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System Identification Using Regular and Quantized Observations
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Springer New York Feb 2013, 2013
ISBN 10: 1461462916
ISBN 13: 9781461462910
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 108 pp. Englisch. Codice articolo 9781461462910
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System Identification Using Regular and Quantized Observations
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents characterizations of identification errors under a probabilistic framework when output sensors are binary, quantized, or regularFirst book devoted to large deviations to system identificationApplication orientedPresents. Codice articolo 4199027
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System Identification Using Regular and Quantized Observations: Applications of Large Deviations Principles
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System Identification Using Regular and Quantized Observations
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Springer New York, Springer New York Feb 2013, 2013
ISBN 10: 1461462916
ISBN 13: 9781461462910
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Taschenbuch. Condizione: Neu. Neuware -¿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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 108 pp. Englisch. Codice articolo 9781461462910
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System Identification Using Regular and Quantized Observations : Applications of Large Deviations Principles
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Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Codice articolo 9781461462910
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System Identification Using Regular and Quantized Observations: Applications of Large Deviations Principles (SpringerBriefs in Mathematics)
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