Space and time complexities and sensor threshold selection in quantized identification

Authors:
Le Yi Wang;G. George Yin;Ji-Feng Zhang;Yanlong Zhao
Affiliations:
Department of Electrical and Computer Engineering, Wayne State University, Detroit, MI 48202, USA;Department of Mathematics, Wayne State University, Detroit, MI 48202, USA;Key Laboratory of Systems and Control, Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China;Key Laboratory of Systems and Control, Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
Venue:
Automatica (Journal of IFAC)
Year:
2008

Citing 9
Cited 2

System identification: theory for the user

System identification: theory for the user
Vector quantization and signal compression

Vector quantization and signal compression
Optimal estimation theory for dynamic systems with set membership uncertainty: an overview

Automatica (Journal of IFAC)
The sample complexity of worst-case identification of FIR linear systems

Systems & Control Letters
Consistent parameter bounding identification for linearly parametrized model sets

Automatica (Journal of IFAC)
Introduction to data compression

Introduction to data compression
Asymptotically efficient parameter estimation using quantized output observations

Automatica (Journal of IFAC)
Identification of Hammerstein Systems with Quantized Observations

SIAM Journal on Control and Optimization
Joint identification of plant rational models and noise distribution functions using binary-valued observations

Automatica (Journal of IFAC)

Identification of Hammerstein Systems with Quantized Observations

SIAM Journal on Control and Optimization
Input design in worst-case system identification with quantized measurements

Automatica (Journal of IFAC)

Quantified Score

Hi-index	22.15

Visualization

Abstract

This work is concerned with system identification of plants using quantized output observations. We focus on relationships between identification space and time complexities. This problem is of importance for system identification in which data-flow rates are limited due to computer networking, communications, wireless channels, etc. Asymptotic efficiency of empirical measure based algorithms yields a tight lower bound on identification accuracy. This bound is employed to derive a separation principle of space and time complexities and to study sensor threshold selection. Insights gained from these understandings provide a feasible approach for optimal utility of communication bandwidth resources in enhancing identification accuracy.