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Learning Dynamical Systems From Quantized Observations: A Bayesian Perspective
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DOI：10.1109/TAC.2021.3122385.png)
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Abstract
En 中文
Identification of dynamical systems from low-resolution quantized observations presents several challenges because of the limited amount of information available in the data and since proper algorithms have to be designed to handle the error due to quantization. In this article, we consider identification of infinite impulse response models from quantized outputs. Algorithms both for maximum-likelihood estimation and Bayesian inference are developed. Finally, a particle-filter approach is presented for recursive reconstruction of the latent nonquantized outputs from past quantized observations.
Keywords:
Maximum likelihood estimation
Bayes methods
Computational modeling
Quantization (signal)
Finite impulse response filters
Data models
Standards
Bayesian inference
maximum likelihood
quantized data
system identification
Journal
IF:
7
Papers:
1.3W
Citations:
6.7W