Higher order statistics of the discrete Wiener model for application in nonlinear process modeling and identification

Author

Hashad, Atalla I. ; Therrien, Charles W.

Author_Institution

Dept. of Electr. & Comput. Eng., Naval Postgraduate Sch., Monterey, CA, USA

Volume

1

fYear

1994

fDate

31 Oct-2 Nov 1994

Firstpage

439

Abstract

The discrete Wiener model of nonlinear systems is used to represent discrete stochastic processes. The higher order statistics of the model output are analysed and shown to have properties that greatly reduce the complexity of their computation. An efficient and structured procedure of computing the cumulants of the output of this model is described. Finally, an example of applying these results in nonlinear system identification and random process modeling is presented

Keywords

computational complexity; higher order statistics; identification; nonlinear systems; random processes; stochastic processes; computation complexity; continuous polyspectra; cumulants; discrete Wiener model; discrete stochastic processes; higher order statistics; nonlinear process modeling; nonlinear system identification; nonlinear systems; random process modeling; Application software; Gaussian processes; Higher order statistics; Impedance matching; Kernel; Linear systems; Nonlinear systems; Random processes; Statistical analysis; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Signals, Systems and Computers, 1994. 1994 Conference Record of the Twenty-Eighth Asilomar Conference on

Conference_Location

Pacific Grove, CA

ISSN

1058-6393

Print_ISBN

0-8186-6405-3

Type

conf

DOI

10.1109/ACSSC.1994.471492

Filename

471492