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Mathematics - Applications | System Identification Using Regular and Quantized Observations - Applications of Large Deviations

System Identification Using Regular and Quantized Observations

Applications of Large Deviations Principles

He, Qi, Wang, Le Yi, Yin, George G.

2013, XII, 95 p. 17 illus., 16 illus. in color.

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  • ​ 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
​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.

Content Level » Research

Keywords » System identification - binary observation - error estimate - large deviations - parameter estimation - quantized observation

Related subjects » Applications - Control Engineering - Probability Theory and Stochastic Processes

Table of contents 

​Introduction and Overview.- System Identification: Formulation.- Large Deviations: An Introduction.- LDP under I.I.D. Noises.- LDP under Mixing Noises.- Applications to Battery Diagnosis.- Applications to Medical Signal Processing.-Applications to Electric Machines.- Remarks and Conclusion.- References.- Index

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