Identification of Wiener Models with Binary-Valued Output Observations

Author

Yanlong Zhao ; Le Yi Wang ; Yin, G.G. ; Ji-Feng Zhang

Author_Institution

Acad. of Math. & Syst. Sci., Chinese Acad. of Sci., Beijing, China

fYear

2006

fDate

7-11 Aug. 2006

Firstpage

423

Lastpage

428

Abstract

This work focuses on system identification for Wiener models, whose outputs are measured by binary sensors. It begins with the development of joint identifiability. Then, using periodic inputs, empirical distributions are used to construct identification algorithms. Convergence of the algorithms is established, and associated recursive algorithms are also developed.

Keywords

Wiener filters; memoryless systems; nonlinear dynamical systems; recursive estimation; Wiener models; binary sensors; binary-valued output observations; joint identifiability; recursive algorithms; system identification; Communication system control; Convergence; Mathematics; Medical control systems; Nonlinear dynamical systems; Process control; Sensor systems; Signal processing; Signal processing algorithms; System identification; Identification; Wiener model; binary-valued observations; joint identifiability;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference, 2006. CCC 2006. Chinese

Conference_Location

Harbin

Print_ISBN

7-81077-802-1

Type

conf

DOI

10.1109/CHICC.2006.280587

Filename

4060550

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3216399