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System Identification Using Regular and Quantized Observations

SBM: SYSTEM IDNTFCTN USING RGLR & QNTZD OBSERVATIONS

Physics

Mathematics & Science

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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.

POST SECONDARY FOUR YEAR

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Front Cover.
Other Frontmatter.
Title Page.
Copyright Page.
Dedication.
Preface.
Contents.
Notation and Abbreviations.
1: Introduction and Overview.
2: System Identification: Formulation.
3: Large Deviations: An Introduction.
4: LDP of System Identification under Independent and Identically Distributed Observation Noises.
5: LDP of System Identification under Mixing Observation Noises.
6: Applications to Battery Diagnosis.
7: Applications to Medical Signal Processing.
8: Applications to Electric Machines.
9: Remarks and Conclusion.
References.
Index.

System Identification Using Regular and Quantized Observations.css-1p8rvzy{position:absolute;padding-left:8px;}

System Identification Using Regular and Quantized Observations