System Identification Using Regular and Quantized Observations - Info and Reading Options
Applications of Large Deviations Principles
By Qi He
"System Identification Using Regular and Quantized Observations" was published by Springer New York in 2013 - New York, NY, it has 95 pages and the language of the book is English.
“System Identification Using Regular and Quantized Observations” Metadata:
- Title: ➤ System Identification Using Regular and Quantized Observations
- Author: Qi He
- Language: English
- Number of Pages: 95
- Publisher: Springer New York
- Publish Date: 2013
- Publish Location: New York, NY
“System Identification Using Regular and Quantized Observations” Subjects and Themes:
- Subjects: ➤ Control - Probability Theory and Stochastic Processes - Mathematics - Distribution (Probability theory) - Control Systems Theory - System theory - System identification - Signal processing - Digital techniques - Signal processing, digital techniques - System analysis
Edition Specifications:
- Format: [electronic resource] :
- Pagination: ➤ XII, 95 p. 17 illus., 16 illus. in color.
Edition Identifiers:
- The Open Library ID: OL27090577M - OL19905427W
- Library of Congress Control Number (LCCN): 2012955366
- ISBN-13: 9781461462927
- All ISBNs: 9781461462927
AI-generated Review of “System Identification Using Regular and Quantized Observations”:
"System Identification Using Regular and Quantized Observations" Table Of Contents:
- 1- 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
- 2- Remarks and Conclusion
- 3- References
- 4- Index.
"System Identification Using Regular and Quantized Observations" Description:
The Open Library:
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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