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)
Introduction to data compression (2nd ed.)
Introduction to data compression (2nd ed.)
Optimization by Vector Space Methods
Optimization by Vector Space Methods
Automatica (Journal of IFAC)
Identification of Wiener systems with binary-valued output observations
Automatica (Journal of IFAC)
Space and time complexities and sensor threshold selection in quantized identification
Automatica (Journal of IFAC)
Tracking and identification of regime-switching systems using binary sensors
Automatica (Journal of IFAC)
Statistical results for system identification based on quantized observations
Automatica (Journal of IFAC)
Identification of Hammerstein Systems with Quantized Observations
SIAM Journal on Control and Optimization
SIAM Journal on Control and Optimization
Input design in worst-case system identification with quantized measurements
Automatica (Journal of IFAC)
Joint state and event observers for linear switching systems under irregular sampling
Automatica (Journal of IFAC)
Hi-index | 22.16 |
This paper studies identification of systems in which only quantized output observations are available. An identification algorithm for system gains is introduced that employs empirical measures from multiple sensor thresholds and optimizes their convex combinations. Strong convergence of the algorithm is first derived. The algorithm is then extended to a scenario of system identification with communication constraints, in which the sensor output is transmitted through a noisy communication channel and observed after transmission. The main results of this paper demonstrate that these algorithms achieve the Cramer-Rao lower bounds asymptotically, and hence are asymptotically efficient algorithms. Furthermore, under some mild regularity conditions, these optimal algorithms achieve error bounds that approach optimal error bounds of linear sensors when the number of thresholds becomes large. These results are further extended to finite impulse response and rational transfer function models when the inputs are designed to be periodic and full rank.