Adaptive identification algorithms for time-varying parameters

Koichi Hidaka, Hiromitsu Ohmori, Akira Sano

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Adaptive identification of rapidly changing parameters is essentially needed in adaptive signal processing and adaptive control. New accelerated LMS and RLS type of adaptive algorithms are given from a stand-point that any parameter changes can be approximately expressed by a finite degree of polynomial function of time. The proposed adaptive algorithm involves ordinary LMS and RLS algorithms as special cases, which are based on the assumption that the parameters are constant but unknown. A sufficient condition for assuring stability of the accelerated adaptive algorithm is clarified based on the small gain and passivity theorems. The effectiveness is examined in numerical simulations and experiments in which adaptive equalization for fading channel and adaptive direction-of-arrival (DOA) tracking experiments.

Original languageEnglish
Title of host publicationProceedings of the IEEE Conference on Decision and Control
Pages4308-4313
Number of pages6
Volume5
Publication statusPublished - 2001
Event40th IEEE Conference on Decision and Control (CDC) - Orlando, FL, United States
Duration: 2001 Dec 42001 Dec 7

Other

Other40th IEEE Conference on Decision and Control (CDC)
CountryUnited States
CityOrlando, FL
Period01/12/401/12/7

Fingerprint

Adaptive algorithms
Direction of arrival
Fading channels
Signal processing
Experiments
Polynomials
Computer simulation

ASJC Scopus subject areas

  • Chemical Health and Safety
  • Control and Systems Engineering
  • Safety, Risk, Reliability and Quality

Cite this

Hidaka, K., Ohmori, H., & Sano, A. (2001). Adaptive identification algorithms for time-varying parameters. In Proceedings of the IEEE Conference on Decision and Control (Vol. 5, pp. 4308-4313)

Adaptive identification algorithms for time-varying parameters. / Hidaka, Koichi; Ohmori, Hiromitsu; Sano, Akira.

Proceedings of the IEEE Conference on Decision and Control. Vol. 5 2001. p. 4308-4313.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Hidaka, K, Ohmori, H & Sano, A 2001, Adaptive identification algorithms for time-varying parameters. in Proceedings of the IEEE Conference on Decision and Control. vol. 5, pp. 4308-4313, 40th IEEE Conference on Decision and Control (CDC), Orlando, FL, United States, 01/12/4.
Hidaka K, Ohmori H, Sano A. Adaptive identification algorithms for time-varying parameters. In Proceedings of the IEEE Conference on Decision and Control. Vol. 5. 2001. p. 4308-4313
Hidaka, Koichi ; Ohmori, Hiromitsu ; Sano, Akira. / Adaptive identification algorithms for time-varying parameters. Proceedings of the IEEE Conference on Decision and Control. Vol. 5 2001. pp. 4308-4313
@inproceedings{49f18fa5bd3a4e3c847d420466d93a86,
title = "Adaptive identification algorithms for time-varying parameters",
abstract = "Adaptive identification of rapidly changing parameters is essentially needed in adaptive signal processing and adaptive control. New accelerated LMS and RLS type of adaptive algorithms are given from a stand-point that any parameter changes can be approximately expressed by a finite degree of polynomial function of time. The proposed adaptive algorithm involves ordinary LMS and RLS algorithms as special cases, which are based on the assumption that the parameters are constant but unknown. A sufficient condition for assuring stability of the accelerated adaptive algorithm is clarified based on the small gain and passivity theorems. The effectiveness is examined in numerical simulations and experiments in which adaptive equalization for fading channel and adaptive direction-of-arrival (DOA) tracking experiments.",
author = "Koichi Hidaka and Hiromitsu Ohmori and Akira Sano",
year = "2001",
language = "English",
volume = "5",
pages = "4308--4313",
booktitle = "Proceedings of the IEEE Conference on Decision and Control",

}

TY - GEN

T1 - Adaptive identification algorithms for time-varying parameters

AU - Hidaka, Koichi

AU - Ohmori, Hiromitsu

AU - Sano, Akira

PY - 2001

Y1 - 2001

N2 - Adaptive identification of rapidly changing parameters is essentially needed in adaptive signal processing and adaptive control. New accelerated LMS and RLS type of adaptive algorithms are given from a stand-point that any parameter changes can be approximately expressed by a finite degree of polynomial function of time. The proposed adaptive algorithm involves ordinary LMS and RLS algorithms as special cases, which are based on the assumption that the parameters are constant but unknown. A sufficient condition for assuring stability of the accelerated adaptive algorithm is clarified based on the small gain and passivity theorems. The effectiveness is examined in numerical simulations and experiments in which adaptive equalization for fading channel and adaptive direction-of-arrival (DOA) tracking experiments.

AB - Adaptive identification of rapidly changing parameters is essentially needed in adaptive signal processing and adaptive control. New accelerated LMS and RLS type of adaptive algorithms are given from a stand-point that any parameter changes can be approximately expressed by a finite degree of polynomial function of time. The proposed adaptive algorithm involves ordinary LMS and RLS algorithms as special cases, which are based on the assumption that the parameters are constant but unknown. A sufficient condition for assuring stability of the accelerated adaptive algorithm is clarified based on the small gain and passivity theorems. The effectiveness is examined in numerical simulations and experiments in which adaptive equalization for fading channel and adaptive direction-of-arrival (DOA) tracking experiments.

UR - http://www.scopus.com/inward/record.url?scp=0035707038&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0035707038&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0035707038

VL - 5

SP - 4308

EP - 4313

BT - Proceedings of the IEEE Conference on Decision and Control

ER -