System Identification Using Regular and Quantized Observations, 1st Edition

  • Published By:
  • ISBN-10: 1461462924
  • ISBN-13: 9781461462927
  • DDC: 003.1
  • Grade Level Range: College Freshman - College Senior
  • 95 Pages | eBook
  • Original Copyright 2013 | Published/Released May 2014
  • This publication's content originally published in print form: 2013

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‚ÄčThis 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.

Table of Contents

Front Cover.
Other Frontmatter.
Title Page.
Copyright Page.
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.