System Identification Using Regular and Quantized Observations

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.

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.

Subjects

Other Editions

• System Identification Using Regular and Quantized Observations: Applications of Large Deviations PrinciplesSpringer New York2013-01-01
• System Identification Using Regular and Quantized ObservationsElectronic resourceSpringer New York2013-01-01
• System Identification Using Regular and Quantized ObservationsPaperbackSpringer2013-02-08