Comparison of algorithms for real-time identification of FIR systems from binary measurements on the output

There are six main algorithms for real-time identification of FIR systems from binary measurements on the output. The objective of this paper is to propose a first short comparison of these methods based on the description of each method and on some numerical simulations. This comparison should help the user in choosing an algorithm if necessary.

[1]  Yanlong Zhao,et al.  Recursive projection algorithm on FIR system identification with binary-valued observations , 2013, Autom..

[2]  Ting Wang,et al.  Adaptive Tracking Control of FIR Systems Under Binary-Valued Observations and Recursive Projection Identification , 2021, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[3]  Le Yi Wang,et al.  Non-commercial Research and Educational Use including without Limitation Use in Instruction at Your Institution, Sending It to Specific Colleagues That You Know, and Providing a Copy to Your Institution's Administrator. All Other Uses, Reproduction and Distribution, including without Limitation Comm , 2022 .

[4]  Yao Yu,et al.  Recursive identification of FIR systems with binary-valued outputs and communication channels , 2015, Autom..

[5]  Guoqi Li,et al.  Identification of Wiener Systems With Clipped Observations , 2012, IEEE Trans. Signal Process..

[6]  Olivier Gehan,et al.  Identification Using Binary Measurements for IIR Systems , 2020, IEEE Transactions on Automatic Control.

[7]  Olivier Gehan,et al.  Continuous-time system identification using binary measurements , 2016, 2016 IEEE 55th Conference on Decision and Control (CDC).

[8]  Jifeng Zhang,et al.  Adaptive Tracking via Binary-Valued Observations with Fixed Threshold* , 2012 .

[9]  Han-Fu Chen Stochastic approximation and its applications , 2002 .

[10]  Le Yi Wang,et al.  Identification of Wiener systems with quantized inputs and binary-valued output observations , 2017, Autom..

[11]  Le Yi Wang,et al.  Joint identification of plant rational models and noise distribution functions using binary-valued observations , 2006, Autom..

[12]  Qijiang Song,et al.  Recursive identification of systems with binary-valued outputs and with ARMA noises , 2018, Autom..

[13]  Le Yi Wang,et al.  Identification of Wiener systems with binary-valued output observations , 2007, Autom..

[14]  Balázs Csanád Csáji,et al.  Recursive Estimation of ARX Systems Using Binary Sensors with Adjustable Thresholds , 2012 .

[15]  Mohammed M'Saad,et al.  Identification of systems using binary sensors via Support Vector Machines , 2015, 2015 54th IEEE Conference on Decision and Control (CDC).

[16]  Le Yi Wang,et al.  Identification Input Design for Consistent Parameter Estimation of Linear Systems With Binary-Valued Output Observations , 2008, IEEE Transactions on Automatic Control.

[17]  Jérôme Juillard,et al.  A recursive system identification method based on binary measurements , 2010, 49th IEEE Conference on Decision and Control (CDC).

[18]  Jérôme Juillard,et al.  A Weighted Least-Squares Approach to Parameter Estimation Problems Based on Binary Measurements , 2010, IEEE Transactions on Automatic Control.

[19]  Jérôme Juillard,et al.  Convergence analysis of an online approach to parameter estimation problems based on binary observations , 2012, Autom..

[20]  Minyue Fu,et al.  Identification of ARMA models using intermittent and quantized output observations , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[21]  Le Yi Wang,et al.  System identification using binary sensors , 2003, IEEE Trans. Autom. Control..

[22]  Minyue Fu,et al.  Identification of ARMA models using intermittent and quantized output observations , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[23]  Olivier Gehan,et al.  Recursive system identification algorithm using binary measurements , 2016, 2016 European Control Conference (ECC).