System Identification Using Regular and Quantized Observations : Applications of Large Deviations Principles / by Qi He, Le Yi Wang, G. George Yin
(SpringerBriefs in Mathematics)
| データ種別 | 電子ブック |
|---|---|
| 出版者 | New York, NY : Springer New York : Imprint: Springer |
| 出版年 | 2013 |
| 本文言語 | 英語 |
| 大きさ | XII, 95 p. 17 illus., 16 illus. in color : online resource |
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| 内容注記 | 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 |
|---|---|
| 一般注記 | 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 |
| 著者標目 | *He, Qi author Wang, Le Yi author Yin, G. George author SpringerLink (Online service) |
| 件 名 | LCSH:Mathematics LCSH:System theory LCSH:Probabilities LCSH:Control engineering FREE:Mathematics FREE:Systems Theory, Control FREE:Control FREE:Probability Theory and Stochastic Processes |
| 分 類 | DC23:519 |
| 巻冊次 | ISBN:9781461462927 
|
| ISBN | 9781461462927 |
| URL | http://dx.doi.org/10.1007/978-1-4614-6292-7 |
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