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.
قائمة المحتويات
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.- Indexقم بشراء هذا الكتاب الإلكتروني واحصل على كتاب آخر مجانًا!
لغة الإنجليزية ● شكل PDF ● صفحات 95 ● ISBN 9781461462927 ● حجم الملف 1.4 MB ● الناشر Springer New York ● مدينة NY ● بلد US ● نشرت 2013 ● للتحميل 24 الشهور ● دقة EUR ● هوية شخصية 2648662 ● حماية النسخ DRM الاجتماعية