System Identification Using Regular and Quantized Observations : Applications of Large Deviations Principles (SpringerBriefs in Mathematics)

He, Q./ Wang, L. Y./ Yin, G. G.

Springer（2013/01発売）

• 製本 Paperback:紙装版/ペーパーバック版／ページ数 95 p.／サイズ 17 illus., 16 in color
• 言語 ENG
• 商品コード 9781461462910
• DDC分類 003

基本説明

This brief presents characterizations of identification errors under a probabilistic framework when output sensors are binary, quantized, or regular.

Full Description

​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.

Contents

​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