System Identification Using Regular and Quantized Observations

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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.
Presents characterizations of identification errors under a probabilistic framework when output sensors are binary, quantized, or regular First book devoted to large deviations to system identification Application oriented

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Qi He

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System identification binary observation error estimate large deviations parameter estimation quantized observation

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