TY - GEN
T1 - Accelerating the rate of convergence for LMS-like on-line identification and adaptation algorithms. Part 1
T2 - 22nd International Conference on Methods and Models in Automation and Robotics, MMAR 2017
AU - Figwer, Jaroslaw
AU - Michalczyk, Malgorzata
AU - Główka, Teresa
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/9/19
Y1 - 2017/9/19
N2 - In the paper a modification enabling acceleration of the rate of convergence for LMS-like on-line identification and adaptation algorithms is proposed. This is based on an artificial decaying of initial conditions in recursive identification as well as adaptation algorithms. The decaying is done using a set of the most recent measurements. Properties of the algorithms with the proposed modification are compared with non-accelerated identification and adaptation algorithms in simulations of a practical adaptive control system.
AB - In the paper a modification enabling acceleration of the rate of convergence for LMS-like on-line identification and adaptation algorithms is proposed. This is based on an artificial decaying of initial conditions in recursive identification as well as adaptation algorithms. The decaying is done using a set of the most recent measurements. Properties of the algorithms with the proposed modification are compared with non-accelerated identification and adaptation algorithms in simulations of a practical adaptive control system.
UR - https://www.scopus.com/pages/publications/85035352133
U2 - 10.1109/MMAR.2017.8046851
DO - 10.1109/MMAR.2017.8046851
M3 - Conference contribution
AN - SCOPUS:85035352133
T3 - 2017 22nd International Conference on Methods and Models in Automation and Robotics, MMAR 2017
SP - 347
EP - 350
BT - 2017 22nd International Conference on Methods and Models in Automation and Robotics, MMAR 2017
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 28 August 2017 through 31 August 2017
ER -