System Identification Using Regular and Quantized Observations Applications of Large Deviations Principles
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...
| Main Authors: | , , |
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| Format: | eBook |
| Language: | English |
| Published: |
New York, NY
Springer New York
2013, 2013
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| Edition: | 1st ed. 2013 |
| Series: | SpringerBriefs in Mathematics
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| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
| Summary: | 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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| Physical Description: | XII, 95 p. 17 illus., 16 illus. in color online resource |
| ISBN: | 9781461462927 |