Blind Volterra signal modeling

Author

Stathaki, Tania

Author_Institution

Signal Process. Section, Imperial Coll. of Sci., Technol. & Med., London, UK

Volume

3

fYear

1996

fDate

7-10 May 1996

Firstpage

1601

Abstract

In this paper the problem of nonlinear signal modeling is examined from a higher-order statistical perspective. The approach taken involves the use of second order Volterra kernels which are derived from a joint operation on second and third order moments of the signal. The paper describes the fundamental issues of the various components of the approach. The nonlinear equations involved are solved by means of unconstrained Lagrange programming neural networks. The results of the entire modeling scheme contained in this paper are very encouraging

Keywords

Volterra series; higher order statistics; neural nets; nonlinear equations; programming; signal processing; signal representation; Volterra series; blind Volterra signal modeling; higher-order statistics; nonlinear equations; nonlinear signal modeling; second order Volterra kernels; second order moments; signal representation; third order moments; unconstrained Lagrange programming neural networks; Educational institutions; Image processing; Lagrangian functions; Neural networks; Nonlinear equations; Nonlinear filters; Nonlinear systems; Random processes; Signal processing; Visual system;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1996. ICASSP-96. Conference Proceedings., 1996 IEEE International Conference on

Conference_Location

Atlanta, GA

ISSN

1520-6149

Print_ISBN

0-7803-3192-3

Type

conf

DOI

10.1109/ICASSP.1996.544109

Filename

544109