DocumentCode
2250867
Title
Strongly consistent recursive regression estimation under depended observations
Author
Chernyshov, K.R.
Author_Institution
Inst. of Control Sci., Moscow, Russia
Volume
5
fYear
2000
fDate
2000
Firstpage
133
Abstract
The paper is focused on establishing strong consistency of recursive estimates of nonlinear characteristics of dynamic systems. To describe the shape of the nonlinearities, the regression function kernel type estimates are used. Within the approach presented, a feature of the technique is considering a case of mutually dependent observations. Simultaneously, only mild and easy verified assumptions with respect to the system´s input and output processes, as well as to the external disturbances, are involved
Keywords
identification; nonlinear systems; recursive estimation; depended observations; nonlinear characteristics; nonlinear dynamic system model; nonlinearity shape; recursive regression estimation; regression function kernel type estimates; strong consistency; system estimation; system identification; Control systems; Kernel; Nonlinear control systems; Nonlinear dynamical systems; Nonlinear systems; Random processes; Recursive estimation; Shape; Stochastic systems; System identification;
fLanguage
English
Publisher
ieee
Conference_Titel
Circuits and Systems, 2000. Proceedings. ISCAS 2000 Geneva. The 2000 IEEE International Symposium on
Conference_Location
Geneva
Print_ISBN
0-7803-5482-6
Type
conf
DOI
10.1109/ISCAS.2000.857381
Filename
857381
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