The discrete Wiener model for representation of nonGaussian stochastic processes

Author

Therrien, Charles W. ; Hashad, Atalla I.

Author_Institution

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

fYear

1993

fDate

1-3 Nov 1993

Firstpage

451

Abstract

The Wiener (1958) model for nonlinear systems is capable of representing a very large class of random processes. In this paper the discrete-time form of this model is developed and it is shown how the moments of any order can be computed for an arbitrary nonlinear process represented by the Wiener model. The moments are expressed in terms of certain normalized “correlation functions” that have a simple and easily computable form. An explicit organized procedure for computing the moments of any order is presented and illustrated in an example

Keywords

nonlinear systems; random processes; stochastic processes; discrete Wiener model; moments; nonGaussian stochastic processes representation; nonlinear process; nonlinear systems; normalized correlation functions; random processes; Convergence; Gaussian processes; Kernel; Linear systems; Memoryless systems; Multidimensional systems; Nonlinear systems; Polynomials; Random processes; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Signals, Systems and Computers, 1993. 1993 Conference Record of The Twenty-Seventh Asilomar Conference on

Conference_Location

Pacific Grove, CA

ISSN

1058-6393

Print_ISBN

0-8186-4120-7

Type

conf

DOI

10.1109/ACSSC.1993.342554

Filename

342554