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.
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.- IndexMua cuốn sách điện tử này và nhận thêm 1 cuốn MIỄN PHÍ!
Ngôn ngữ Anh ● định dạng PDF ● Trang 95 ● ISBN 9781461462927 ● Kích thước tập tin 1.4 MB ● Nhà xuất bản Springer New York ● Thành phố NY ● Quốc gia US ● Được phát hành 2013 ● Có thể tải xuống 24 tháng ● Tiền tệ EUR ● TÔI 2648662 ● Sao chép bảo vệ DRM xã hội