A unified identification algorithm of FIR systems based on binary observations with time-varying thresholds

Parameter estimation of quantized DARMA systems using weighted least squares

“…The authors in [13] employed Bayesian framework and Markov Chain Monte Carlo methods to study linear system identification with quantized output data. A kind of sign‐error type unified algorithm with projection was exploited for FIR systems with binary observations using time‐varying thresholds in [20] with bounded noises and independent and identically distributed (i.i.d.) stochastic noises, respectively.…”

“…In contrast to the previous works [14–19], the main contributions of this paper are summarized as follows: To the best of my knowledge, plenty of literatures ([1, 9, 10, 12, 13, 20] etc.) on parameter estimate by quantized data do not involve regressor terms (output signals), which obviously cannot generalize the majority of discrete‐time linear time‐invariant systems.…”

“…A considerable amount of work (see [1,5,[9][10][11][12][13][14][15][16][17][18][19][20][21]) on identification, estimation and control based on different types of quantized data (binary data, set-valued data, uniform data etc.) has been made in the past two decades.…”