Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
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. стр. 2134-2139 (IEEE Conference on Decision and Control).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
}
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