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Combined Procedure with Randomized Controls for the Parameters' Confidence Region of Linear Plant under External Arbitrary Noise. / Amelin, K.; Amelina, N.; Granichin, O.; Granichina, O.

2012 IEEE 51ST ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC). IEEE Canada, 2012. p. 2134-2139 (IEEE Conference on Decision and Control).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Harvard

Amelin, K, Amelina, N, Granichin, O & Granichina, O 2012, Combined Procedure with Randomized Controls for the Parameters' Confidence Region of Linear Plant under External Arbitrary Noise. in 2012 IEEE 51ST ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC). IEEE Conference on Decision and Control, IEEE Canada, pp. 2134-2139, 51st IEEE Annual Conference on Decision and Control (CDC), 10/12/12. <http://www.math.spbu.ru/user/gran/papers/1495CDC12.pdf>

APA

Amelin, K., Amelina, N., Granichin, O., & Granichina, O. (2012). Combined Procedure with Randomized Controls for the Parameters' Confidence Region of Linear Plant under External Arbitrary Noise. In 2012 IEEE 51ST ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC) (pp. 2134-2139). (IEEE Conference on Decision and Control). IEEE Canada. http://www.math.spbu.ru/user/gran/papers/1495CDC12.pdf

Vancouver

Amelin K, Amelina N, Granichin O, Granichina O. Combined Procedure with Randomized Controls for the Parameters' Confidence Region of Linear Plant under External Arbitrary Noise. In 2012 IEEE 51ST ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC). IEEE Canada. 2012. p. 2134-2139. (IEEE Conference on Decision and Control).

Author

Amelin, K. ; Amelina, N. ; Granichin, O. ; Granichina, O. / Combined Procedure with Randomized Controls for the Parameters' Confidence Region of Linear Plant under External Arbitrary Noise. 2012 IEEE 51ST ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC). IEEE Canada, 2012. pp. 2134-2139 (IEEE Conference on Decision and Control).

BibTeX

@inproceedings{a730b3df256246c7933d330d7d830525,
title = "Combined Procedure with Randomized Controls for the Parameters' Confidence Region of Linear Plant under External Arbitrary Noise",
abstract = "The new algorithm is proposed for the estimating of linear plant's unknown parameters in the case of observations with arbitrary external noises. It is based on adding of randomized inputs (test perturbations) through the feedback channel. The assumptions about the noise are reduced to a minimum: it can virtually be arbitrary but independently of it the user must be able to add test perturbations. We combine the previous result about asymptotic properties of randomized control strategy with the new one which is followed by a non-asymptotic approach of LSCR (Leave-out Sign-dominant Correlation Regions) method. The new algorithm gives confidence regions for series of finite sets of observations. These regions shrink to the true values of an unknown parameters when number of observations tents to infinity while the algorithm complexity does not increases.",
keywords = "IDENTIFICATION, SYSTEMS",
author = "K. Amelin and N. Amelina and O. Granichin and O. Granichina",
year = "2012",
language = "Английский",
series = "IEEE Conference on Decision and Control",
publisher = "IEEE Canada",
pages = "2134--2139",
booktitle = "2012 IEEE 51ST ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC)",
address = "Канада",
note = "null ; Conference date: 10-12-2012 Through 13-12-2012",

}

RIS

TY - GEN

T1 - Combined Procedure with Randomized Controls for the Parameters' Confidence Region of Linear Plant under External Arbitrary Noise

AU - Amelin, K.

AU - Amelina, N.

AU - Granichin, O.

AU - Granichina, O.

PY - 2012

Y1 - 2012

N2 - The new algorithm is proposed for the estimating of linear plant's unknown parameters in the case of observations with arbitrary external noises. It is based on adding of randomized inputs (test perturbations) through the feedback channel. The assumptions about the noise are reduced to a minimum: it can virtually be arbitrary but independently of it the user must be able to add test perturbations. We combine the previous result about asymptotic properties of randomized control strategy with the new one which is followed by a non-asymptotic approach of LSCR (Leave-out Sign-dominant Correlation Regions) method. The new algorithm gives confidence regions for series of finite sets of observations. These regions shrink to the true values of an unknown parameters when number of observations tents to infinity while the algorithm complexity does not increases.

AB - The new algorithm is proposed for the estimating of linear plant's unknown parameters in the case of observations with arbitrary external noises. It is based on adding of randomized inputs (test perturbations) through the feedback channel. The assumptions about the noise are reduced to a minimum: it can virtually be arbitrary but independently of it the user must be able to add test perturbations. We combine the previous result about asymptotic properties of randomized control strategy with the new one which is followed by a non-asymptotic approach of LSCR (Leave-out Sign-dominant Correlation Regions) method. The new algorithm gives confidence regions for series of finite sets of observations. These regions shrink to the true values of an unknown parameters when number of observations tents to infinity while the algorithm complexity does not increases.

KW - IDENTIFICATION

KW - SYSTEMS

M3 - статья в сборнике материалов конференции

T3 - IEEE Conference on Decision and Control

SP - 2134

EP - 2139

BT - 2012 IEEE 51ST ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC)

PB - IEEE Canada

Y2 - 10 December 2012 through 13 December 2012

ER -

ID: 74016301